It can be shown that there exist various connections to information bottleneck ideas as well as learning a generative model using variational EM algorithms. The two winners of the dynamics category highlight essential characteristics of memory-based meta-learning (more general than just RL) as well as on-policy RL: - Non-Staggered Meta-Learner’s Dynamics (Rabinowitz, 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. Best Deep Learning Research of 2019 So Far. 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. We have broken down the best paper from ICML 2019 into easy-to-understand sections in this article Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS. Try your hands at them and let us know what you accomplish. Instead, they conceptualize the experts as nonlinear feedback controllers around a single nominal trajectory. 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. Everyone - with enough compute power - can do PPO with crazy batchsizes. Planning may then be done by unrolling the deterministic dynamics model in the latent space given the embedded observation. Schrittwieser, J., I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, et al. Our day to day life is filled with situations which require anticipation & Theory of Mind. Traditionally, Model-Based RL has been struggling with learning the dynamics of high-dimensional state spaces. But it is human made & purposed to increase our quality of life. Still there have been some major theoretical breakthroughs revolving around new discoveries (such as Neural Tangent Kernels). “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. Dreamer learns by propagating “analytical” gradients of learned state values through imagined trajectories of a world model. NeurIPS is THE premier machine learning conference in the world. 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). 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 … Hopefully, this gives you some insights into the machine and deep learning research space in 2019. 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.. The results in this study show that recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on language modelling, unsupervised parsing, targeted syntactic evaluation, and logical inference. The outer learning loop thereby corresponds to learning an optimal prior for rapid adaptation during the inner loop. 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). Customer Services. These learning-curve step transitions are associated with a staggered discovery (& unlearning!) In this article, I’ve conducted an informal survey of all the deep reinforcement learning research thus far in 2019 and I’ve picked out some of my favorite papers. This is the course for which all other machine learning courses are judged. Recently, there have been several advances in understanding the learning dynamics of Deep Learning & Stochastic Gradient Descent. Disclaimer: I did not read every DRL paper from 2019 (which would be quite the challenge). Joint learning induces a form of non-stationarity in the environment which is the core challenge of Multi-Agent RL (MARL). - Information Asymmetry in KL-Regularized RL (Galashov et al., 2019). 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). Few-shot learning has been regarded as the crux of intelligence. Usually a lot of the model capacity had to be “wasted” on non-relevant parts of the state space (e.g. 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. More specifically, stochastic gradients of multi-step returns are efficiently propagated through neural network predictions using the re-parametrization trick. But this is definitely not all there is. Importantly, the expert policies are not arbitrary pre-trained RL agents, but 2 second snippets of motion capture data. Domain Randomization has been proposed to obtain a robust policy. Deep learning (DL) is playing an increasingly important role in our lives. 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. And these are my two favorite approaches: MuZero provides the next iteration in removing constraints from the AlphaGo/AlphaZero project. The entire architecture is trained end-to-end using BPTT & outperforms AlphaGo as well as ATARI baselines in the low sample regime. Low-level dexterity, on the other hand, a capability so natural to us, provides a major challenge for current systems. These include the findings on staggered task discovery (e.g., Saxe et al., 2013; Rahaman et al., 2018). I tried to choose the winners for the first category based on the scientific contributions and not only the massive scaling of already existing algorithms. 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. 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)”. ADR aims to design a curriculum of environment complexities to maximize learning progress. In other words, relatively more transparent and less black-box kind of training. ISBI 2019 AWARDS. They don’t only significantly stabilize learning but also allow for larger learning rates & bigger epochs. Interestingly, being able to model rewards, values & policies appears to be all that is needed to plan effectively. So this is my personal top 10 - let me know if I missed your favorite paper! ICLR considers a variety of topics for the conference, such as: In 2019, machine learning and deep learning will be an invaluable asset for the modern marketing professional to keep their services competitive. The action can thereby be thought of as a bottleneck between a future trajectory and a past latent state. Hafner, D., T. Lillicrap, J. Ba, and M. Norouzi, Jaques, N., A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. I’ve tried to include both links to the original papers and their code where possible. They then log the Jacobian at every action-state pair and optimize a pertubation objective which resembles a form of denoising autoencoder. What strikes us the most is how this paper proposes an elegant new approach to the old problem. Merel et al. (2019) cast this intuition in the realm of deep probabilistic models. The representation learning problem is decomposed into iteratively learning a representation, transition and reward model. 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 model boils down to an autoregressive latent-variable model of state-conditional action sequences. 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. Highlights of the Project This paper is an attempt to establish rigorous benchmarks for image classifier robustness. The key idea is to reward actions that lead to relatively higher change in other agents’ behavior. AI conferences like NeurIPS, ICML, ICLR, ACL and MLDS, among others, attract scores of interesting papers every year. The authors also demonstrate that these new variants can eliminate the generalisation gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. 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. All in all 2019 has highlighted the immense potential of Deep RL in previously unimagined dimensions. Unlike supervised learning where the training data is somewhat given and treated as being IID (independent and identically distributed), RL requires an agent to generate their own training data. And, propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier’s robustness to common perturbations. Finally, a few interesting observations regarding large-scale implementation: Learning dynamics in Deep RL remain far from being understood. Now this is one amazing paper! In the final paper of todays post, Merel et al. Learning an Animatable Detailed 3D Face Model from In-The-Wild Images. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. I would love to know how severe the interference problem is in classical on-policy continuous control tasks. Or so we thought . In the words of the authors: “When a new successful strategy or mutation emerges, it changes the implicit task distribution neighboring agents need to solve and creates a new pressure for adaptation.”. It has been well known that Deep Learning is equipped to solve tasks which require the extraction & manipulation of high-level features. The agents undergo 6 distinct phases of dominant strategies where shifts are based on the interaction with tools in the environment. LISTA (learned iterative shrinkage-thresholding algorithm), have been an empirical success for sparse signal recovery. Instead of training a single agent, PBT trains a population with different hyperparameters in parallel. This already becomes apparent in a simplistic society of two agent GAN training. Good thing that there are people working on increasing the sample (but not necessarily computational) efficiency via hallucinating in a latent space. Autoregressive models are known to generate small images unconditionally but a problem arises when these methods are applied to generate large images. To help you quickly get up to speed on the latest ML trends, we’re introducing our research series, […] Deep Reinforcement Learning. This work had also been awarded the ‘best paper’ award. 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. Please feel free to pull requests or open an issue to add papers… I don’t want to know the electricity bill, OpenAI & DeepMind have to pay. 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. The authors state that PBT may shield against such detrimental on-policy effect. Optimisation is then performed by alternating between gradient descent updates of $\pi$ (standard KL objective - regularization) and $\pi_0$ (supervised learning given trajectories of $\pi$ - distillation). Nonetheless, the training is performed using multi-agent self-play and the most simplistic reward one can imagine: Survival in a multi-agent game of hide-and-seek. The year 2019 saw an increase in the number of submissions. 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 tool is Intel Nervana’s Python-based deep learning library. 2018 was a busy year for deep learning based Natural Language Processing (NLP) research. The hiders learn a division of labor - due to team-based rewards. 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. 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. - OpenAI’s Solving’ of the Rubik’s Cube (OpenAI, 2019). More recently, there have multiple proposals to do planning/imagination in an abstract space (i.e., an Abstract MDP). Best Paper Awards. 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). This is an extremely competitive list (50/22,000 or 0.23% chance), and carefully picks the most useful Machine Learning … The empirical validation is performed on contextual bandits. This was an observation already made in the MA-DDPG paper by. While FTW uses a prior based on a time-scale hierarchy of two LSTMs, AlphaStar makes use of human demonstrations. 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. The best way to stay current in this crazy world, apart from reading cool books, is reading important papers on the subject. Time that is costly & could otherwise be used to generate more (but noisy) transitions in environment. Here is an infographic showing top contributors. Best Resources to Learn Machine Learning and Apply It to Finance: Books, Courses, and YouTube (2019) By Denis Kryukov ... question is a pressing issue for both IT and finance professionals — so we’ve compiled an exhaustive list of the best resources to learn machine learning and apply it to finance. A mechanism that might enable such flexibility is the modular reuse of subroutines. 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. via Oreilly This year… In order to give this post a little more structure, I decided to group the papers into 5 main categories and selected a winner as well as runner-up. The KL-regularized expected reward objective can then be rewritten such that the divergence is computed between the policy of the agent $\pi$ and a default policy $\pi_0$ which receives partial inputs. [Related Article: The Most Influential Deep Learning Research of 2019] A survey on intrinsic motivation in reinforcement learning. Vinyals, O., I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, et al. The highlighted large-scale projects remain far from sample efficient. Deep learning is playing a major role in helping businesses improve their customer services. Dreamer, on the other hand, provides a principled extension to continuous action spaces that is able to tame long-horizon tasks based on high-dimensional visual inputs. Or to be more precise, it focuses on an algo… In this blog post I want to share some of my highlights from the 2019 literature. Instead I tried to distill some key narratives as well as stories that excite me. 1. But these problems are being addressed by the current hunt for effective inductive biases, priors & model-based approaches. Naive independent optimization via gradient descent is prone to get stuck in local optima. The authors test the proposed intrinsic motivation formulation in a set of sequential social dilemma and provide evidence for enhanced emergent coordination. The deep learning framework Region based Convolutional Neural Network(RCNN) is implemented for the recognition of vehicles with region proposals. In this article, we will focus on the 5 papers that left a really big impact on us in this year. A total of 774 papers got accepted for ICML 2019 out of 3424 initial submissions (22.6% acceptance rate). The GitHub URL is here: neon. 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. The course uses the open-source programming language Octave instead of Python or R for the assignments. 2019, on the other hand, proved that we are far from having reached the limits of combining function approximation with reward-based target optimization. I have a master's degree in Robotics and I write…. & Geoffrey H. (2015) (Cited: 5,716) Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. This field attracts one of the most productive research groups globally. it is better to learn deep learning from online courses than from books. Usually, the large action space of DeepMindLab is reduced by a human prior (or bias). Finally, they get rid of centralized access to other agents policies by having agents learn to predict each others behavior, a soft-version of Theory of Mind. the Deadly Triad), something anyone who has toyed around with DQNs will have experienced. In several experiments it is shown that this may lead to reusable behavior is sparse reward environments. The authors introduce an autoencoder architecture with latent variational bottleneck to distill a large set of expert policies in a latent embedding space. Also, I am personally especially excited about how this might relate to evolutionary methods such as Population-Based Training (PBT). Below is a list of top 10 papers everyone was talking about, covering DeepFakes, Facial Recognition, Reconstruction, & more. The 2019 edition witnessed over fifteen hundred submissions of which 524 papers were accepted. 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. of skills and the path is caused by a coupling of learning and data generation arising due to on-policy rollouts, hence an interference. Thereby, the general MCTS + function approximation toolbox is opened to more general problem settings such as vision-based problems (such as ATARI). Large batch-sizes are very important when training a centralized controller in MARL. The overall optimization process is interleaved by training an actor-critic-based policy using imagined trajectories. Understanding nature by using mathematics as a tool is one of the finest abilities of human beings. This is reminiscent of Bayes-optimal inference & provides evidence for a connection between meta-learning & Empirical Bayes. Source: Top 5 Deep Learning Research Papers in 2019 In this blog post I want to share some of my highlights from the 2019 literature. Reference Paper IEEE 2019 A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance System Finally, it might help us design learning signals which allow for fast adaptation. 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. Given such a powerful ‘motor primitive’ embedding, one still has to obtain the student policy given the expert rollouts. Deep Learning, by Yann L., Yoshua B. Paper Session 3: Deep Learning for Recommender Systems. Specifically, it overcomes the endorsement of the transition dynamics. Prior to this the most high profile incumbent was Word2Vec which was first published in 2013. That is impressive. Like every PhD novice I got to spend a lot of time reading papers, implementing cute ideas & getting a feeling for the big questions. Date: Tuesday, Sept 17, 2019, 11:00-12:30 Location: Auditorium Chair: Giovanni Semeraro Best Deep Learning Research of 2019 So Far ... 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. ... 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. 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. The authors state that planning in latent space also opens up the application of MCTS in environments with stochastic transitions - pretty exciting if you ask me. This ability is rarely intuitive and has to be learned through inferring, learning axioms, symbols, relations and properties. 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. ICLR considers a variety of topics for the conference, such as: Here are few works (in no particular order) presented at the recently concluded ICLR conference at New Orleans, US, which make an attempt at pushing the envelope of deep learning to newer boundaries: Usually, Long short-term memory (LSTM) architectures allow different neurons to track information at different time scales but they do not have an explicit bias towards modelling a hierarchy of constituents. This constrains the agent to learn one thing at a time while parallel learning of individual contexts would be beneficial. The following two papers propose two distinct ways: Simultaneous learning of a goal-agnostic default policy & learning a dense embedding space that is able to represent a large set of expert behaviors. As before the next action is selected based on the MCTS rollout & sampling proportionately to the visit count. The problem is reduced to a regression which predicts rewards, values & policies & the learning of a representation function $h_\theta$ which maps an observation to an abstract space, a dynamics function $g_\theta$ as well as a policy and value predictor $f_\theta$. 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. Furthermore, when allowing for vector-valued communication, social influence reward-shaping results in informative & sparse communication protocols. Akkaya, I., M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, et al. But honestly, what is more impressive: In-hand manipulation with crazy reward sparsity or learning a fairly short sequence of symbolic transformations? The KL divergence between marginal and other-agent’s-action conditional policies can then be seen as a measure of social influence. Fourth, find papers with released source code and read code. This tool provides high performance with its ease-of-use and extensibility features. - NPMP: Neural Probabilistic Motor Primitives (Merel et al., 2019). deep learning IEEE PAPER 2019 IEEE PAPERS AND PROJECTS FREE TO DOWNLOAD . 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). 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. Partial observability, long time-scales as well vast action spaces remained illusive. 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. It requires vast amounts of generalization & we humans do it all the time. In this work, the researchers, discover ways to enhance corruption and perturbation robustness. Simulators only capture a limited set of mechanisms in the real world & accurately simulating friction demands computation time. This paper attempts to address this question. The authors provide an approach to leverage repeated structure in learning problems. 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 … The author empirically establishes that the meta-learning inner loop undergoes very different dynamics. Personally, I really enjoyed how much DeepMind and especially Oriol Vinyals cared for the StarCraft community. The International Conference on Learning Representations (ICLR) is one of the highly regarded deep learning conferences conducted every year at the end of spring. My favorite contribution of OpenAI’s dexterity efforts is Automatic Domain Randomization (ADR): A key challenge for training Deep RL agents on robotic tasks is to transfer what was learned in simulation to the physical robot. NeurIPS 2019was the 33rd edition of the conference, held between 8th and 14th December in Vancouver, Canada. Finally, the authors also compare different representation learning methods (reward prediction, pixel reconstruction & contrastive estimation/observation reconstruction) and show that pixel reconstruction usually outperforms constrastive estimation. Their experiments show that this is able to distill 2707 experts & perform effective one-shot transfer resulting in smooth behaviors. 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. Third, read slightly older but seminal papers (one or two years old) with many citations. 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. (2019) argue against a behavioral cloning perspective since this often turns out either sample inefficient or non-robust. - Dreamer (aka. Without further ado, here is my top 10 DRL papers from 2019. 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). Tags: the most outer pixels of an ATARI frame) which was rarely relevant to success. This can lead to significant instabilities (e.g. In the motor control literature it has therefore been argued for a set of motor primitives/defaults which can be efficiently recomposed & reshaped. Bayes-Optimal inference & provides evidence for enhanced emergent coordination experts & perform effective one-shot transfer resulting in behaviors. Manipulation of high-level features the findings on staggered task discovery ( e.g., Saxe et al. 2019. The realm of deep learning library otherwise be used to generate large images classifier ’ s Solving ’ the! Environment complexities to maximize learning progress observations of all agents enables more robust feedback signals the... ; Rahaman et al., 2019 ) argue against a behavioral cloning perspective since this often turns out sample. New approach to obtain a robust policy this was an observation already made in the real world accurately... Flexibility is the core challenge of multi-agent RL ( MARL ) where possible us... 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