Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination). In this work, we propose What's In My Big Data? (WIMBD), a platform and a set of sixteen analyses that allow us to reveal and compare the contents of large text corpora. WIMBD builds on two basic capabilities -- count and search -- at scale, which allows us to analyze more than 35 terabytes on a standard compute node. We apply WIMBD to ten different corpora used to train popular language models, including C4, The Pile, and RedPajama. Our analysis uncovers several surprising and previously undocumented findings about these corpora, including the high prevalence of duplicate, synthetic, and low-quality content, personally identifiable information, toxic language, and benchmark contamination. For instance, we find that about 50% of the documents in RedPajama and LAION-2B-en are duplicates. In addition, several datasets used for benchmarking models trained on such corpora are contaminated with respect to important benchmarks, including the Winograd Schema Challenge and parts of GLUE and SuperGLUE. We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them: github.com/allenai/wimbd.

The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 72%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org.

Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases) instead of focusing on relevant information in each modality. That a unimodal model achieves similar accuracy on a VL task to a multimodal one, indicates that so-called unimodal collapse occurred. However, accuracy-based tests fail to detect e.g., when the model prediction is wrong, while the model used relevant information from a modality. Instead, we propose MM-SHAP, a performance-agnostic multimodality score based on Shapley values that reliably quantifies in which proportions a multimodal model uses individual modalities. We apply MM-SHAP in two ways: (1) to compare models for their average degree of multimodality, and (2) to measure for individual models the contribution of individual modalities for different tasks and datasets. Experiments with six VL models – LXMERT, CLIP and four ALBEF variants – on four VL tasks highlight that unimodal collapse can occur to different degrees and in different directions, contradicting the wide-spread assumption that unimodal collapse is one-sided. Based on our results, we recommend MM-SHAP for analysing multimodal tasks, to diagnose and guide progress towards multimodal integration. Code available at https://github.com/Heidelberg-NLP/MM-SHAP .

Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP's data by filtering with its model parameters. In this work, we intend to reveal CLIP's data curation approach and in our pursuit of making it open to the community introduce Metadata-Curated Language-Image Pre-training (MetaCLIP). MetaCLIP takes a raw data pool and metadata (derived from CLIP's concepts) and yields a balanced subset over the metadata distribution. Our experimental study rigorously isolates the model and training settings, concentrating solely on data. MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data, while maintaining the same training budget, attains 72.4%. Our observations hold across various model sizes, exemplified by ViT-H achieving 80.5%, without any bells-and-whistles. Curation code and training data distribution on metadata is made available at https://github.com/facebookresearch/MetaCLIP .

There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamples, a wide spread belief in their iterative self-critique capabilities persists. In this paper, we set out to systematically investigate the effectiveness of iterative prompting of LLMs in the context of Graph Coloring, a canonical NP-complete reasoning problem that is related to propositional satisfiability as well as practical problems like scheduling and allocation. We present a principled empirical study of the performance of GPT4 in solving graph coloring instances or verifying the correctness of candidate colorings. In iterative modes, we experiment with the model critiquing its own answers and an external correct reasoner verifying proposed solutions. In both cases, we analyze whether the content of the criticisms actually affects bottom line performance. The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solution--and thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticisms--whether by LLMs or external solvers--seems largely irrelevant to the performance of iterative prompting. We show that the observed increase in effectiveness is largely due to the correct solution being fortuitously present in the top-k completions of the prompt (and being recognized as such by an external verifier). Our results thus call into question claims about the self-critiquing capabilities of state of the art LLMs.

This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrained using classification objectives to contrastively (SigLIP) pretrained ones. We find that, while slightly underperforming on standard image classification benchmarks, SigLIP-based PaLI shows superior performance across various multimodal benchmarks, especially on localization and visually-situated text understanding. We scale the SigLIP image encoder up to 2 billion parameters, and achieves a new state-of-the-art on multilingual cross-modal retrieval. We hope that PaLI-3, at only 5B parameters, rekindles research on fundamental pieces of complex VLMs, and could fuel a new generation of scaled-up models.

Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others. The challenge is to use a single model for performing diverse vision-language tasks effectively with simple multi-modal instructions. Towards this objective, we introduce MiniGPT-v2, a model that can be treated as a unified interface for better handling various vision-language tasks. We propose using unique identifiers for different tasks when training the model. These identifiers enable our model to better distinguish each task instruction effortlessly and also improve the model learning efficiency for each task. After the three-stage training, the experimental results show that MiniGPT-v2 achieves strong performance on many visual question-answering and visual grounding benchmarks compared to other vision-language generalist models. Our model and codes are available at https://minigpt-v2.github.io/

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.

I wonder what a paper like this, especially given the title, does for the legal case regarding copyright and generative AI. Haven't had a chance to read the paper yet, so don't know if the findings are relevant to copyright.

The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data. However, as these models become larger and more complex, they also become more challenging to train and deploy. One approach to addressing this challenge is the use of sparsely-gated mixture-of-experts (MoE) techniques, which divide the model into smaller, specialized sub-models that can jointly solve a task. In this paper, we explore the effectiveness of MoE in scaling vision-language models, demonstrating its potential to achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling VLMs. We hope our work will inspire further research into the use of MoE for scaling large-scale vision-language models and other multimodal machine learning applications.

In recent times there has been a surge of multi-modal architectures based on Large Language Models, which leverage the zero shot generation capabilities of LLMs and project image embeddings into the text space and then use the auto-regressive capacity to solve tasks such as VQA, captioning, and image retrieval. We name these architectures as "bridge-architectures" as they project from the image space to the text space. These models deviate from the traditional recipe of training transformer based multi-modal models, which involve using large-scale pre-training and complex multi-modal interactions through co or cross attention. However, the capabilities of bridge architectures have not been tested on complex visual reasoning tasks which require fine grained analysis about the image. In this project, we investigate the performance of these bridge-architectures on the NLVR2 dataset, and compare it to state-of-the-art transformer based architectures. We first extend the traditional bridge architectures for the NLVR2 dataset, by adding object level features to faciliate fine-grained object reasoning. Our analysis shows that adding object level features to bridge architectures does not help, and that pre-training on multi-modal data is key for good performance on complex reasoning tasks such as NLVR2. We also demonstrate some initial results on a recently bridge-architecture, LLaVA, in the zero shot setting and analyze its performance.

Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locations, ambiguities, and variations in the real-world environment can be better described in human language, naturally governed by grammatical rules and other modalities such as audio and depth. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. These models are referred to as foundational models. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions. In this survey, we provide a comprehensive review of such emerging foundational models, including typical architecture designs to combine different modalities (vision, text, audio, etc), training objectives (contrastive, generative), pre-training datasets, fine-tuning mechanisms, and the common prompting patterns; textual, visual, and heterogeneous. We discuss the open challenges and research directions for foundational models in computer vision, including difficulties in their evaluations and benchmarking, gaps in their real-world understanding, limitations of their contextual understanding, biases, vulnerability to adversarial attacks, and interpretability issues. We review recent developments in this field, covering a wide range of applications of foundation models systematically and comprehensively. A comprehensive list of foundational models studied in this work is available at https://github.com/awaisrauf/Awesome-CV-Foundational-Models .

Multilingual Vision-Language Pre-training (VLP) is a promising but challenging topic due to the lack of large-scale multilingual image-text pairs. Existing works address the problem by translating English data into other languages, which is intuitive and the generated data is usually limited in form and scale. In this paper, we explore a more practical and scalable setting: weakly supervised multilingual VLP with only English image-text pairs and multilingual text corpora. We argue that the universal multilingual representation learned from texts allows the cross-modal interaction learned in English to be transferable to other languages. To this end, we propose a framework to effectively unify cross-lingual and cross-modal pre-training. For unified modeling on different data, we design an architecture with flexible modules to learn different interactions. Moreover, two unified tasks are introduced to efficiently guide the unified cross-lingual cross-modal learning. Extensive experiments demonstrate that our pre-trained model learns universal multilingual multimodal representations, allowing effective cross-lingual transfer on multimodal tasks. Code and models are available at https://github.com/FudanDISC/weakly-supervised-mVLP.

The development of language models have moved from encoder-decoder to decoder-only designs. In addition, we observe that the two most popular multimodal tasks, the generative and contrastive tasks, are nontrivial to accommodate in one architecture, and further need adaptations for downstream tasks. We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. This is done with a simple model, called MaMMUT. It consists of a single vision encoder and a text decoder, and is able to accommodate contrastive and generative learning by a novel two-pass approach on the text decoder. We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Furthermore, the same architecture enables straightforward extensions to open-vocabulary object detection and video-language tasks. The model tackles a diverse range of tasks, while being modest in capacity. Our model achieves the state of the art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models. It shows very competitive results on VQA and Video Captioning, especially considering its capacity. Ablations confirm the flexibility and advantages of our approach.

Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling. Transformer models have greatly improved performance and versatility over previous vision language models. They do so by pretraining models on a large generic datasets and transferring their learning to new tasks with minor changes in architecture and parameter values. This type of transfer learning has become the standard modeling practice in both natural language processing and computer vision. Vision language transformers offer the promise of producing similar advancements in tasks which require both vision and language. In this paper, we provide a broad synthesis of the currently available research on vision language transformer models and offer some analysis of their strengths, limitations and some open questions that remain.

Meta releases SeamlessM4T, a general multilingual speech/text model claimed to surpass OpenAI's Whisper. It's available on github and everything can be used for free in a non-commercial setting.

Model Features:

• Automatic speech recognition for ~100 languages.
• Speech-to-text translation for ~100 input/output languages.
• Speech-to-speech translation for ~100 input languages and 35 output languages.
• Text-to-text and text-to-speech translation for nearly 100 languages.

Dataset:

• SeamlessAlign: Open multimodal translation dataset with 270,000 hours of speech and text alignments.

Technical Insights:

• Utilizes a multilingual and multimodal text embedding space for 200 languages.
• Applied a teacher-student approach to extend this embedding space to the speech modality, covering 36 languages.
• Mining performed on publicly available repositories resulted in 443,000 hours of speech aligned with texts and 29,000 hours of speech-to-speech alignments.

Toxicity Filter:

• The model identifies toxic words from speech inputs/outputs and filters unbalanced toxicity in training data.
• The demo detects toxicity in both input and output. If toxicity is only detected in the output, a warning is included and the output is not shown.
• Given how impaired llama2-chat has been due to these kind of filters, it's unclear how useful these models are in a general setting.

Hugging Face released IDEFICS, an 80B open-access visual language model replicating DeepMind's unreleased Flamingo. Built entirely on public data, it's the first of its size available openly. Part of its training utilized OBELICS, a dataset with 141M web pages, 353M images, and 115B text tokens from Common Crawl.

• Announcement
• Demo
• Models
• OBELICS Dataset
• OBELICS Paper
• Lessons

We introduce VisIT-Bench (Visual InsTruction Benchmark), a benchmark for evaluation of instruction-following vision-language models for real-world use. Our starting point is curating 70 'instruction families' that we envision instruction tuned vision-language models should be able to address. Extending beyond evaluations like VQAv2 and COCO, tasks range from basic recognition to game playing and creative generation. Following curation, our dataset comprises 592 test queries, each with a human-authored instruction-conditioned caption. These descriptions surface instruction-specific factors, e.g., for an instruction asking about the accessibility of a storefront for wheelchair users, the instruction-conditioned caption describes ramps/potential obstacles. These descriptions enable 1) collecting human-verified reference outputs for each instance; and 2) automatic evaluation of candidate multimodal generations using a text-only LLM, aligning with human judgment. We quantify quality gaps between models and references using both human and automatic evaluations; e.g., the top-performing instruction-following model wins against the GPT-4 reference in just 27% of the comparison. VisIT-Bench is dynamic to participate, practitioners simply submit their model's response on the project website; Data, code and leaderboard is available at visit-bench.github.io .

Achieves SOTA on quality AND on training time AND renders in real-time (60fps+)

MOOCs

Nowadays, there are a couple of really excellent online lectures to get you started. The list is too long to include them all. Every one of the major MOOC sites offers not only one but several good Machine Learning classes, so please check coursera, edX, Udacity yourself to see which ones are interesting to you.

However, there are a few that stand out, either because they're very popular or are done by people who are famous for their work in ML. Roughly in order from easiest to hardest, those are:

• Andrew Ng's ML-Class at coursera: Focused on application of techniques. Easy to understand, but mathematically very shallow. Good for beginners!
  https://www.coursera.org/course/ml

• Hasti/Tibshirani's Statistical Learning
  https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about

• Yaser Abu-Mostafa's Learning From Data: Focuses a lot more on theory, but also doable for beginners
  https://work.caltech.edu/telecourse.html

• Geoff Hinton's Neural Nets for Machine Learning: As the title says, this is almost exclusively about Neural Networks.
  https://www.coursera.org/course/neuralnets

• Hugo Larochelle's Neural Net lectures: Again mostly on Neural Nets, with a focus on Deep Learning
  http://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH

• Daphne Koller's Probabilistic Graphical Models Is a very challenging class, but has a lot of good material that few of the other MOOCs here will cover
  https://www.coursera.org/course/pgm

Books

The most often recommended textbooks on general Machine Learning are (in no particular order):

• Hasti/Tibshirani/Friedman's Elements of Statistical Learning FREE
  http://statweb.stanford.edu/%7Etibs/ElemStatLearn/

• Barber's Bayesian Reasoning and Machine Learning FREE
  http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

• MacKay's Information Theory, Inference and Learning Algorithms FREE
  http://www.inference.phy.cam.ac.uk/itila/book.html

• Goodfellow/Bengio/Courville's Deep Learning FREE
  http://www.deeplearningbook.org/

• Nielsen's Neural Networks and Deep Learning FREE
  http://neuralnetworksanddeeplearning.com/

• Graves' Supervised Sequence Labelling with Recurrent Neural Networks FREE
  http://www.cs.toronto.edu/%7Egraves/preprint.pdf

• Sutton/Barto's Reinforcement Learning: An Introduction; 2nd Edition FREE
  https://www.dropbox.com/s/7jl597kllvtm50r/book2015april.pdf

Note that these books delve deep into math, and might be a bit heavy for complete beginners. If you don't care so much about derivations or how exactly the methods work but would rather just apply them, then the following are good practical intros:

• An Introduction to Statistical Learning FREE
  http://www-bcf.usc.edu/%7Egareth/ISL/

• Probabilistic Programming and Bayesian Methods for Hackers FREE
  http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb

There are of course a whole plethora on books that only cover specific subjects, as well as many books about surrounding fields in Math. A very good list has been collected by /u/ilsunil here

Deep Learning Resources

• Karpathy's Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Lecture Notes)
  http://cs231n.github.io/

• Video Lecture's Stanford CS231n: Convolutional Neural Networks for Visual Recognition
  https://www.youtube.com/playlist?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA

• Silver's Reinforcement Learning Lectures
  https://www.youtube.com/watch?v=2pWv7GOvuf0

• Colah's Informational Blog
  http://colah.github.io/

• Bruna's UC Berkeley Stat212b: Topics Course on Deep Learning
  https://joanbruna.github.io/stat212b/

• Overview of Neural Network Architectures
  http://www.asimovinstitute.org/neural-network-zoo/

Math Resources

• Strang's Linear Algebra Lectures
  https://www.youtube.com/watch?v=ZK3O402wf1c

• Kolter/Do's Linear Algebra Review and Reference Notes
  http://cs229.stanford.edu/section/cs229-linalg.pdf

• Calculus 1
  https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x

• Introduction to Probability
  https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1

Programming Languages and Software

In general, the most used languages in ML are probably Python, R and Matlab (with the latter losing more and more ground to the former two). Which one suits you better depends wholy on your personal taste. For R, a lot of functionality is either already in the standard library or can be found through various packages in CRAN. For Python, NumPy/SciPy are a must. From there, Scikit-Learn covers a broad range of ML methods.

If you just want to play around a bit and don't do much programming yourself then things like Visions of Chaos, WEKA, KNIME or RapidMiner might be of your liking. Word of caution: a lot of people in this subreddit are very critical of WEKA, so even though it's listed here, it is probably not a good tool to do anything more than just playing around a bit. A more detailed discussion can be found here

Deep Learning Software, GPU's and Examples

There are a number of modern deep learning toolkits you can utilize to implement your models. Below, you will find some of the more popular toolkits. This is by no means an exhaustive list. Generally speaking, you should utilize whatever GPU has the most memory, highest clock speed, and most CUDA cores available to you. This was the NVIDIA Titan X from the previous generation. These frameworks are all very close in computation speed, so you should choose the one you prefer in terms of syntax.

Theano is a python based deep learning toolkit developed by the Montreal Institute of Learning Algorithms, a cutting edge deep learning academic research center and home of many users of this forum. This has a large number of tutorials ranging from beginner to cutting edge research.

Torch is a Luajit based scientific computing framework developed by Facebook Artificial Intelligence Research (FAIR) and is also in use at Twitter Cortex. There is the torch blog which contains examples of the torch framework in action.

TensorFlow is a python deep learning framework developed by Google Brain and in use at Google Brain and Deepmind. The newest framework around. Some TensorFlow examples may be found here Do not ask questions on the Google Groups, ask them on stackoverflow

Neon is a python based deep learning framework built around a custom and highly performant CUDA compiler Maxas by NervanaSys.

Caffe is an easy to use, beginner friendly deep learning framework. It provides many pretrained models and is built around a protobuf format of implementing neural networks.

Keras can be used to wrap Theano or TensorFlow for ease of use.

Datasets and Challenges for Beginners

There are a lot of good datasets here to try out your new Machine Learning skills.

• Kaggle has a lot of challenges to sink your teeth into. Some even offer prize money!
  http://www.kaggle.com/

• The UCI Machine Learning Repository is a collection of a lot of good datasets
  http://archive.ics.uci.edu/ml/

• http://blog.mortardata.com/post/67652898761/6-dataset-lists-curated-by-data-scientists lists some more datasets

• Here is a very extensive list of large-scale datasets of all kinds.
  http://www.quora.com/Data/Where-can-I-find-large-datasets-open-to-the-public

• Another dataset list
  http://www.datawrangling.com/some-datasets-available-on-the-web

Research Oriented Datasets

In many papers, you will find a few datasets are the most common. Below, you can find the links to some of them.

• MNIST A short handwriting dataset that is often used as a sanity check in modern research
  http://yann.lecun.com/exdb/mnist/

• SVHN Similar to MNIST, but with color numbers. A sanity check in most cases.
  http://ufldl.stanford.edu/housenumbers/

• CIFAR-10/0 CIFAR 10 and 100 are two natural color images that are often used with convolutional neural networks for image classification.
  https://www.cs.toronto.edu/%7Ekriz/cifar.html

Communities

• http://www.datatau.com/ is a data-science centric hackernews

• http://metaoptimize.com/qa/ and http://stats.stackexchange.com/ are Stackoverflow-like discussion forums

ML Research

Machine Learning is a very active field of research. The two most prominent conferences are without a doubt NIPS and ICML. Both sites contain the pdf-version of the papers accepted there, they're a great way to catch up on the most up-to-date research in the field. Other very good conferences include UAI (general AI), COLT (covers theoretical aspects) and AISTATS.

Good journals for ML papers are the Journal of Machine Learning Research, the Journal of Machine Learning and arxiv.

Other sites and Tutorials

• http://datasciencemasters.org/ is an extensive list of lectures and textbooks for a whole Data Science curriculum

• http://deeplearning.net/

• http://en.wikipedia.org/wiki/Machine_learning

• http://videolectures.net/Top/Computer_Science/Machine_Learning/

FAQ

• How much Math/Stats should I know?

That depends on how deep you want to go. For a first exposure (e.g. Ng's Coursera class) you won't need much math, but in order to understand how the methods really work,having at least an undergrad level of Statistics, Linear Algebra and Optimization won't hurt.