... We’re just about finished with Q1 of 2019, and the research side of deep learning technology is forging ahead at a very good clip. The teacher and creator of this course for beginners is Andrew Ng, a Stanford professor, co-founder of Google Brain, co-founder of Coursera, and the VP that grew Baidu’s AI team to thousands of scientists.. 2019 - What a year for Deep Reinforcement Learning (DRL) research - but also my first year as a PhD student in the field. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014. Understanding the dynamics of Meta-Learning (e.g., Wang et al., 2016) & the relationship between outer- and inner-loop learning, on the other hand, remains illusive. But this is definitely not all there is. - Dreamer (aka. The 200 deep learning papers I read in 2019. I would love to know how severe the interference problem is in classical on-policy continuous control tasks. And, to address the challenge of the sheer difficulty of learning a distribution that preserves both global semantic coherence and exactness of detail, the researchers propose to use multidimensional upscaling to grow an image in both size and depth via intermediate stages corresponding to distinct SPNs. It can be shown that there exist various connections to information bottleneck ideas as well as learning a generative model using variational EM algorithms. Often times science fiction biases our perception towards thinking that ML is an arms race. The outer learning loop thereby corresponds to learning an optimal prior for rapid adaptation during the inner loop. Date: Tuesday, Sept 17, 2019, 11:00-12:30 Location: Auditorium Chair: Giovanni Semeraro Importantly, the expert policies are not arbitrary pre-trained RL agents, but 2 second snippets of motion capture data. Below is a list of top 10 papers everyone was talking about, covering DeepFakes, Facial Recognition, Reconstruction, & more. Instead of training the agent on a single environment with a single set of environment-generating hyperparameters, the agent is trained on a plethora of different configurations. Woj Zaremba mentioned at the ‘Learning Transferable Skills’ workshop at NeurIPS 2019 that it took them one day to ‘solve the cube’ with DRL & that it is possible to do the whole charade fully end-to-end. The expert demonstrations are used to pre-train the policy of the agent via supervised minimization of a KL objective & provide an efficient regularization to ensure that the exploration behavior of the agent is not drowned by StarCraft’s curse of dimensionality. Given a current history and a small look-ahead snippet, the model has to predict the action that enables such a transition (aka an inverse model). The agents undergo 6 distinct phases of dominant strategies where shifts are based on the interaction with tools in the environment. The scientific contributions include a unique version of prioritized fictitious self-play (aka The League), an autoregressive decomposition of the policy with pointer networks, upgoing policy update (UPGO - an evolution of the V-trace Off-Policy Importance Sampling correction for structured action spaces) as well as scatter connections (a special form of embedding that maintains spatial coherence of the entities in map layer). The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). Naive independent optimization via gradient descent is prone to get stuck in local optima. No other research conference attracts a crowd of 6000+ people in one place – it is truly elite in its scope. Their main ambition is to extract representations which are able to not only encode key dimensions of behavior but are also easily recalled during execution. The authors test the proposed intrinsic motivation formulation in a set of sequential social dilemma and provide evidence for enhanced emergent coordination. In 2019, machine learning and deep learning will be an invaluable asset for the modern marketing professional to keep their services competitive. This is one of the two papers which got top honours at ICLR 2019. ... Second, I should skim through the highly scored or best paper nominees of major conferences. The authors show how such a simplistic reward structure paired with self-play can lead to self-supervised skill acquisition that is more efficient than intrinsic motivation. I have a master's degree in Robotics and I write about machine learning advancements. However, there is no comparable benchmark for cooperative multi-agent RL. Few-shot learning has been regarded as the crux of intelligence. While traditional approaches to intrinsic motivation often have been ad-hoc and manually defined, this paper introduces a causal notion of social empowerment via pseudo-rewards resulting from influential behavior. Z. Leibo, and N. De Freitas, Baker, B., I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch, Schaul, T., D. Borsa, J. Modayil, and R. Pascanu, Galashov, A., S. M. Jayakumar, L. Hasenclever, D. Tirumala, J. Schwarz, G. Desjardins, W. M. Czarnecki, Y. W. Teh, R. Pascanu, and N. Heess, Merel, J., L. Hasenclever, A. Galashov, A. Ahuja, V. Pham, G. Wayne, Y. W. Teh, and N. Heess, Lowe, R., Y. Wu, A. Tamar, J. Harb, O. In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. This tool is Intel Nervana’s Python-based deep learning library. In the final paper of todays post, Merel et al. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. This paper is an attempt to establish rigorous benchmarks for image classifier robustness. PlaNet 2.0; Hafner et al., 2019). Best Paper Awards. Furthermore, when allowing for vector-valued communication, social influence reward-shaping results in informative & sparse communication protocols. Due to the existence of region proposal in RCNN, computational multiplicity is reduced. of skills and the path is caused by a coupling of learning and data generation arising due to on-policy rollouts, hence an interference. The action can thereby be thought of as a bottleneck between a future trajectory and a past latent state. Our day to day life is filled with situations which require anticipation & Theory of Mind. deep learning IEEE PAPER 2019 IEEE PAPERS AND PROJECTS FREE TO DOWNLOAD . These are only a few of the accepted papers and it is obvious that the researchers from Google, Microsoft, MIT, Berkeley are one of the top contributors and collaborators for many works. Like every PhD novice I got to spend a lot of time reading papers, implementing cute ideas & getting a feeling for the big questions. More specifically, stochastic gradients of multi-step returns are efficiently propagated through neural network predictions using the re-parametrization trick. The two selected MARL papers highlight two central points: Going from the classical centralized-training + decentralized control paradigm towards social reward shaping & the scaled use and unexpected results of self-play: - Social Influence as Intrinsic Motivation (Jaques et al., 2019). One of the findings from this work is how consistent are the winning tickets that are less than 10-20% of fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. The deep learning framework Region based Convolutional Neural Network(RCNN) is implemented for the recognition of vehicles with region proposals. This can lead to significant instabilities (e.g. A. I. P. Abbeel, and I. Mordatch, Saxe, A. M., J. L. McClelland, and S. Ganguli, Rahaman, N., A. Baratin, D. Arpit, F. Draxler, M. Lin, F. A. Hamprecht, Y. Bengio, and A. Courville, Wang, J. X., Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Agency goes beyond the simplistic paradigm of central control. The entire architecture is trained end-to-end using BPTT & outperforms AlphaGo as well as ATARI baselines in the low sample regime. And, the results show that anything above this threshold leads to the winning tickets learning faster than the original network and attains higher test accuracy. Good thing that there are people working on increasing the sample (but not necessarily computational) efficiency via hallucinating in a latent space. “And the first place in the category ‘Large-Scale DRL Projects’ goes to…” (insert awkward opening of an envelope with a microphone in one hand) + : DeepMind’s AlphaStar project led by Oriol Vinyals. 1. The hiders learn a division of labor - due to team-based rewards. Reference Paper IEEE 2019 A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance System In the motor control literature it has therefore been argued for a set of motor primitives/defaults which can be efficiently recomposed & reshaped. Here, the authors propose a lottery ticket hypothesis which states that dense, randomly-initialised, feed-forward networks contain subnetworks (winning tickets) that — when trained in isolation — reach test accuracy comparable to the original network in a similar number of iterations. This is the course for which all other machine learning courses are judged. Large batch-sizes are very important when training a centralized controller in MARL. There are major problems, but the impact that one can have is proportionately great. In this work, the researchers, discover ways to enhance corruption and perturbation robustness. Check out the full list of accepted papers, Google Open-Sources Robot.txt To Help Standardise Robots Exclusion Protocol, Guide To Google’s AudioSet Datasets With Implementation in PyTorch, After-Effects Of Timnit Gebru’s Layoff — Industry Reactions, Guide To LibriSpeech Datasets With Implementation in PyTorch and TensorFlow, Hands-on Guide To Synthetic Image Generation With Flip, MIT Develops A New System That Creates Different Kind Of Robots, Guide To Dataturks – The Human-in-the-Loop Data Annotation Platform, Full-Day Hands-on Workshop on Fairness in AI, Machine Learning Developers Summit 2021 | 11-13th Feb |. I don’t want to know the electricity bill, OpenAI & DeepMind have to pay. These findings are of importance whenever the actual learning behaviour of a system is of importance (e.g., curriculum learning, safe exploration as well human-in-the-loop applications). The 2019 edition witnessed over fifteen hundred submissions of which 524 papers were accepted. While reading the Nature paper, I realized that the project is very much based on the FTW setup used to tackle Quake III: Combine a distributed IMPALA actor-learner setting with a powerful prior that induces structured exploration. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. But honestly, what is more impressive: In-hand manipulation with crazy reward sparsity or learning a fairly short sequence of symbolic transformations? NeurIPS is THE premier machine learning conference in the world. By automatically increasing/decreasing the range of possible environment configurations based on the learning progress of the agent, ADR provides a pseudo-natural curriculum for the agent. If you want to immerse yourself in the latest machine learning research developments, you need to follow NeurIPS. LISTA (learned iterative shrinkage-thresholding algorithm), have been an empirical success for sparse signal recovery. This emergence of an autocurriculum and disctinct plateus of dominant strategies ultimately led to unexpected solutions (such as surfing on objects). 2018 was a busy year for deep learning based Natural Language Processing (NLP) research. 2019 - What a year for Deep Reinforcement Learning (DRL) research - but also my first year as a PhD student in the field. Best machine learning paper award: Aniket Pramanik and colleagues from the University of Iowa, USA for the paper “Off-The-Grid Model Based Deep Learning (O-MoDL)”. So this is my personal top 10 - let me know if I missed your favorite paper! The authors derive an analytical relationship to dynamical systems and show a connection to saddle point transitions. We have broken down the best paper from ICML 2019 into easy-to-understand sections in this article I am excited for what there is to come in 2020 & believe that it is an awesome time to be in the field. But these problems are being addressed by the current hunt for effective inductive biases, priors & model-based approaches. (2019) cast this intuition in the realm of deep probabilistic models. 2019, on the other hand, proved that we are far from having reached the limits of combining function approximation with reward-based target optimization. The overall optimization process is interleaved by training an actor-critic-based policy using imagined trajectories. There is no better time to live than the present. Deep Learning, by Yann L., Yoshua B. Or to be more precise, it focuses on an algo… The representation learning problem is decomposed into iteratively learning a representation, transition and reward model. In several experiments it is shown that this may lead to reusable behavior is sparse reward environments. deep learning 2019 IEEE PAPERS AND PROJECTS FREE TO DOWNLOAD . Challenges such as Quake III/’Capture the Flag’, StarCraft II, Dota 2 as well as robotic hand manipulation highlight only a subset of exciting new domains which modern DRL is capable of tackling. And, propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier’s robustness to common perturbations. Partial observability, long time-scales as well vast action spaces remained illusive. Merel et al. Our DECA (Detailed Expression Capture and Animation) model is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict … Finally, it might help us design learning signals which allow for fast adaptation. Also, I am personally especially excited about how this might relate to evolutionary methods such as Population-Based Training (PBT). Their experiments show that this is able to distill 2707 experts & perform effective one-shot transfer resulting in smooth behaviors. Best Deep Learning Research of 2019 So Far. The global deep learning market is forecast to maintain its growing momentum throughout 2019, while the world’s top 10 deep learning companies are expected to continue their market leadership over next five years. The key idea is to reward actions that lead to relatively higher change in other agents’ behavior. I religiously follow this confere… The algorithm did not ‘fully’ learn end-to-end what the right sequence of moves is to solve a cube & then do the dexterous manipulation required. CSE ECE EEE IEEE. In a nutshell, this paper attacks one of the most influential machine learning problems – the problem of learning an unknown halfspace. The highlighted large-scale projects remain far from sample efficient. Instead of learning based on a non-informative knowledge base, the agent can rely upon previously distilled knowledge in the form of a prior distribution But how may one obtain such? And these are my two favorite approaches: MuZero provides the next iteration in removing constraints from the AlphaGo/AlphaZero project. Disclaimer: I did not read every DRL paper from 2019 (which would be quite the challenge). email:firstname.lastname@example.org, Copyright Analytics India Magazine Pvt Ltd, Economic Survey 2019 Shows The Government’s Rising Interest Towards Data. One approach to obtain effective and fast-adapting agents, are informed priors. The GitHub URL is here: neon. Interestingly, being able to model rewards, values & policies appears to be all that is needed to plan effectively. (2019) argue against a behavioral cloning perspective since this often turns out either sample inefficient or non-robust. Source: Top 5 Deep Learning Research Papers in 2019 We constantly assume the reaction of other individuals and readjust our beliefs based on recent evidence. Best of Arxiv.org for AI, Machine Learning, and Deep Learning – January 2019 (insidebigdata.com) ... Reviewers are just people reading papers, if it's hard to reproduce a paper's results, they can't verify that they are correct. Still there have been some major theoretical breakthroughs revolving around new discoveries (such as Neural Tangent Kernels). While the previous two projects are exciting show-cases of the potential for DRL, they are ridiculously sample-inefficient. If you couldn’t make it to CVPR 2019, no worries. The authors show that this can be circumvented by learning a default policy which constrains the action spaces & thereby reduces the complexity of the exploration problem. While FTW uses a prior based on a time-scale hierarchy of two LSTMs, AlphaStar makes use of human demonstrations. In this paper, the authors propose the Subscale Pixel Network (SPN), a conditional decoder architecture that generates an image as a sequence of image slices of equal size. The concept of influence is thereby grounded in a counterfactual assessment: How would another agent’s action change if I had acted differently in this situation. Ray interference is a phenomenon observed in (multi-objective) Deep RL when learning dynamics travel through a sequence of plateaus. The authors state that PBT may shield against such detrimental on-policy effect. In this paper, Analytic LISTA (ALISTA) is proposed, where the weight matrix in LISTA is computed as the solution to a data-free optimisation problem, leaving only the step size and threshold parameters to data-driven learning. Domain Randomization has been proposed to obtain a robust policy. In this article, we will focus on the 5 papers that left a really big impact on us in this year. The authors present methods to evaluate this ability through the structured nature of the mathematics domain to enable the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures. I have a master's degree in Robotics and I write…. Instead I tried to distill some key narratives as well as stories that excite me. Astonishingly, this (together with a PPO-LSTM-GAE-based policy) induces a form of meta-learning that apparently appears to have not yet reached its full capabilities (by the time of publishing). Joint learning induces a form of non-stationarity in the environment which is the core challenge of Multi-Agent RL (MARL). Third, read slightly older but seminal papers (one or two years old) with many citations. The course uses the open-source programming language Octave instead of Python or R for the assignments. Highlights of the Project Thereby, the general MCTS + function approximation toolbox is opened to more general problem settings such as vision-based problems (such as ATARI). Without further ado, here is my top 10 DRL papers from 2019. Instead, they conceptualize the experts as nonlinear feedback controllers around a single nominal trajectory. To help you quickly get up to speed on the latest ML trends, we’re introducing our research series, […] Planning may then be done by unrolling the deterministic dynamics model in the latent space given the embedded observation. Paper Session 3: Deep Learning for Recommender Systems. In this paper, the DeepMind researchers investigate the mathematical reasoning abilities of neural models. The 2019 edition witnessed over fifteen hundred submissions of which 524 papers were accepted. the most outer pixels of an ATARI frame) which was rarely relevant to success. - DeepMind’s AlphaStar (Vinyals et al, 2019). Thereby, an ensemble can generate a diverse of experiences which may overcome plateaus through the diversity of population members. PlaNet 2.0; Hafner et al., 2019), Social Influence as Intrinsic Motivation (Jaques et al., 2019), Autocurricula & Emergent Tool-Use (OpenAI, 2019), Non-Staggered Meta-Learner’s Dynamics (Rabinowitz, 2019), Information Asymmetry in KL-Regularized RL (Galashov et al., 2019), NPMP: Neural Probabilistic Motor Primitives (Merel et al., 2019), Grandmaster level in StarCraft II using multi-agent reinforcement learning, Mastering ATARI, Go, Chess and Shogi by planning with a learned model, Dream to Control: Learning Behaviors by Latent Imagination, Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning, Reward Shaping for Decentralized Training, Emergent tool use from multi-agent autocurricula, Environment Curriculum Learning for Multi-Agent Setups, Meta-learners’ learning dynamics are unlike learners’, Empirical characterization of Meta-Learner’s inner loop dynamics, Ray Interference: a Source of Plateaus in Deep Reinforcement Learning, Analytical derivation of plateau phenomenon in on-policy RL, Information asymmetry in KL-regularized RL, Neural probabilistic motor primitives for humanoid control. Prior to this the most high profile incumbent was Word2Vec which was first published in 2013. - NPMP: Neural Probabilistic Motor Primitives (Merel et al., 2019). Most of pre-2019 breakthrough accomplishments of Deep RL (e.g., ATARI DQNs, AlphaGo/Zero) have been made in domains with limited action spaces, fully observable state spaces as well as moderate credit assignment time-scales. Personally, I really enjoyed how much DeepMind and especially Oriol Vinyals cared for the StarCraft community. Source: Deep Learning on Medium #ODSC – Open Data ScienceApr 23We’re just about finished with Q1 of 2019, and the research side of deep learning technology is forging ahead at a … This work had also been awarded the ‘best paper’ award. Like every PhD novice I got to spend a lot of time reading papers, implementing cute ideas & getting a feeling for the big questions. 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