mtybilly/Instruct2DS huggingface dataset:
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
Authors: Jongbin Ryu, Gitaek Kwon, Ming-Hsuan Yang, Jongwoo Lim
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance. Nevertheless, it is typically difficult for existing random forest methods to strike a good balance between these conflicting factors. In this work, we propose a generalized convolutional forest networks to learn a feature space to maximize the strength of individual tree classifiers while minimizing the respective correlation. The feature space is iteratively constructed by a probabilistic triplet sampling method based on the distribution obtained from the splits of the random forest. The sampling process is designed to pull the data of the same label together for higher strength and push away the data frequently falling to the same leaf nodes. We perform extensive experiments on five image classification and two domain generalization datasets with ResNet-50 and DenseNet-161 backbone networks. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
Evaluating The Search Phase of Neural Architecture Search
Authors: Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: 
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based on their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we propose to evaluate the NAS search phase.
To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy; (ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process.
We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones.
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Authors: AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alan Aspuru-Guzik
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
Authors: Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice reinforcement learning can be very time-consuming or even infeasible unless the reward function is shaped so as to provide a smooth gradient towards a successful outcome. This shaping is difficult to specify by hand, particularly when the task is learned from raw observations, such as images. In this paper, we study how we can automatically learn dynamical distances: a measure of the expected number of time steps to reach a given goal state from any other state. These dynamical distances can be used to provide well-shaped reward functions for reaching new goals, making it possible to learn complex tasks efficiently. We show that dynamical distances can be used in a semi-supervised regime, where unsupervised interaction with the environment is used to learn the dynamical distances, while a small amount of preference supervision is used to determine the task goal, without any manually engineered reward function or goal examples. We evaluate our method both on a real-world robot and in simulation. We show that our method can learn to turn a valve with a real-world 9-DoF hand, using raw image observations and just ten preference labels, without any other supervision. Videos of the learned skills can be found on the project website: https://sites.google.com/view/dynamical-distance-learning
Principled Weight Initialization for Hypernetworks
Authors: Oscar Chang, Lampros Flokas, Hod Lipson
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner. Despite extensive applications ranging from multi-task learning to Bayesian deep learning, the problem of optimizing hypernetworks has not been studied to date. We observe that classical weight initialization methods like Glorot & Bengio (2010) and He et al. (2015), when applied directly on a hypernet, fail to produce weights for the mainnet in the correct scale. We develop principled techniques for weight initialization in hypernets, and show that they lead to more stable mainnet weights, lower training loss, and faster convergence.
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
Authors: Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
Authors: Zuyue Fu, Zhuoran Yang, Yongxin Chen, Zhaoran Wang
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost. We consider the setting where the agents have identical linear state transitions and quadratic cost func- tions, while the aggregated effect of the agents is captured by the population mean of their states, namely, the mean-field state. For such a game, based on the Nash certainty equivalence principle, we provide sufficient conditions for the existence and uniqueness of its Nash equilibrium. Moreover, to find the Nash equilibrium, we propose a mean-field actor-critic algorithm with linear function approxima- tion, which does not require knowing the model of dynamics. Specifically, at each iteration of our algorithm, we use the single-agent actor-critic algorithm to approximately obtain the optimal policy of the each agent given the current mean- field state, and then update the mean-field state. In particular, we prove that our algorithm converges to the Nash equilibrium at a linear rate. To the best of our knowledge, this is the first success of applying model-free reinforcement learn- ing with function approximation to discrete-time mean-field Markov games with provable non-asymptotic global convergence guarantees.
Learning Compositional Koopman Operators for Model-Based Control
Authors: Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods. Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators. These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects. In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects. The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal. Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines.
Controlling generative models with continuous factors of variations
Authors: Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Recent deep generative models can provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing. Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor understanding of the learned representation. To overcome these major issues, very recent works have shown the interest of studying the semantics of the latent space of generative models. In this paper, we propose to advance on the interpretability of the latent space of generative models by introducing a new method to find meaningful directions in the latent space of any generative model along which we can move to control precisely specific properties of the generated image like position or scale of the object in the image. Our method is weakly supervised and particularly well suited for the search of directions encoding simple transformations of the generated image, such as translation, zoom or color variations. We demonstrate the effectiveness of our method qualitatively and quantitatively, both for GANs and variational auto-encoders.
Spectral  Embedding of Regularized Block Models
Authors: Nathan De Lara, Thomas Bonald
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Spectral embedding is a popular technique for the representation of graph data. Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering. In this paper, we explain on a simple block model the impact of the complete graph regularization, whereby a constant is added to all entries of the adjacency matrix. Specifically, we show that the regularization forces the spectral embedding  to focus on  the  largest blocks, making the representation less sensitive to noise or outliers. We illustrate these results on both  on both synthetic and real data, showing how regularization improves standard clustering scores.
Robust Local Features for Improving the Generalization of Adversarial Training
Authors: Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained models often lack adversarially robust generalization on unseen testing data. Recent works show that adversarially trained models are more biased towards global structure features. Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation. To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples. We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples. To demonstrate the generality of our argument, we implement RLFAT in currently state-of-the-art adversarial training frameworks. Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training. Additionally, we demonstrate that our models capture more local features of the object on the images, aligning better with human perception.
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
Authors: Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: We give a tight characterization of the (vectorized Euclidean) norm of weights required to realize a function $f:\mathbb{R}\rightarrow \mathbb{R}^d$ as a single hidden-layer ReLU network with an unbounded number of units (infinite width), extending the univariate characterization of Savarese et al. (2019) to the multivariate case.
Classification-Based Anomaly Detection for General Data
Authors: Liron Bergman, Yedid Hoshen
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware
Authors: Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, Lei Zhang
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization. Despite plenty of prior work on the quantization of weights or activations for neural networks, there is still a wide gap between the software quantizers and the low-precision accelerator implementation, which degrades either the efficiency of networks or that of the hardware for the lack of software and hardware coordination at design-phase. In this paper, we propose a learned linear symmetric quantizer for integer neural network processors, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators (e.g., 16-bit) and multipliers (e.g., 4-bit). We use a unified way to quantize weights and activations, and the results outperform many previous approaches for various networks such as AlexNet, ResNet, and lightweight models like MobileNet while keeping friendly to the accelerator architecture. Additional, we also apply the method to object detection models and witness high performance and accuracy in YOLO-v2. Finally, we deploy the quantized models on our specialized integer-arithmetic-only DNN accelerator to show the effectiveness of the proposed quantizer. We show that even with linear symmetric quantization, the results can be better than asymmetric or non-linear methods in 4-bit networks. In evaluation, the proposed quantizer induces less than 0.4\% accuracy drop in ResNet18, ResNet34, and AlexNet when quantizing the whole network as required by the integer processors.
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Authors: Suraj Nair, Chelsea Finn
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor scalability of sampling-based planning optimizers, one significant limitation of these approaches is the ability to plan over long horizons to reach distant goals. To that end, we propose a framework for subgoal generation and planning, hierarchical visual foresight (HVF), which generates subgoal images conditioned on a goal image, and uses them for planning. The subgoal images are directly optimized to decompose the task into easy to plan segments, and as a result, we observe that the method naturally identifies semantically meaningful states as subgoals. Across three out of four simulated vision-based manipulation tasks, we find that our method achieves more than 20% absolute performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes.
From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech
Authors: Hyeong-Seok Choi, Changdae Park, Kyogu Lee
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations. To this end, we propose a multi-modal learning framework that links the inference stage and generation stage. First, the inference networks are trained to match the speaker identity between the two different modalities. Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice. The proposed method exploits the recent development of GANs techniques and generates the human face directly from the speech waveform making our system fully end-to-end. We analyze the extent to which the network can naturally disentangle two latent factors that contribute to the generation of a face image one that comes directly from a speech signal and the other that is not related to it and explore whether the network can learn to generate natural human face image distribution by modeling these factors. Experimental results show that the proposed network can not only match the relationship between the human face and speech, but can also generate the high-quality human face sample conditioned on its speech. Finally, the correlation between the generated face and the corresponding speech is quantitatively measured to analyze the relationship between the two modalities.
Data-Independent Neural Pruning via Coresets
Authors: Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample.

We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework, which finds a small weighted subset of points that provably approximates the original inputs. Specifically, we approximate the output of a layer of neurons by a coreset of neurons in the previous layer and discard the rest. We apply this framework in a layer-by-layer fashion from the top to the bottom. Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input $x\in \mathbb{R}^d$, including an adversarial one. We demonstrate the effectiveness of our method on popular network architectures. In particular, our coresets yield 90% compression of the LeNet-300-100 architecture on MNIST while improving the accuracy.
Adversarially Robust Representations with Smooth Encoders
Authors: Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy (Dj) Dvijotham, Pushmeet Kohli
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data. We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO). We show that the ELBO fails to control the behaviour of the encoder out of the support of the empirical data distribution and this behaviour of the VAE can lead to extreme errors in the learned representation. This is a key hurdle in the effective use of representations for data-efficient learning and transfer. To address this problem, we propose to augment the data with specifications that enforce insensitivity of the representation with respect to families of transformations. To incorporate these specifications, we propose a regularization method that is based on a selection mechanism that creates a fictive data point by explicitly perturbing an observed true data point. For certain choices of parameters, our formulation naturally leads to the minimization of the entropy regularized Wasserstein distance between representations. We illustrate our approach on standard datasets and experimentally show that significant improvements in the downstream adversarial accuracy can be achieved by learning robust representations completely in an unsupervised manner, without a reference to a particular downstream task and without a costly supervised adversarial training procedure.
Contrastive Learning of Structured World Models
Authors: Thomas Kipf, Elise van der Pol, Max Welling
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Authors: Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever "stale"), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling -- achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs. 

This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially "solves" the task -- near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS+Compass sensor.  Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs). Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks -- the analog of "ImageNet pre-training + task-specific fine-tuning" for embodied AI. Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as a universal resource (all models and code are publicly available).
Neural Arithmetic Units
Authors: Andreas Madsen, Alexander Rosenberg Johansen
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Neural networks can approximate complex functions, but they struggle to perform exact arithmetic operations over real numbers. The lack of inductive bias for arithmetic operations leaves neural networks without the underlying logic necessary to extrapolate on tasks such as addition, subtraction, and multiplication. We present two new neural network components: the Neural Addition Unit (NAU), which can learn exact addition and subtraction; and the Neural Multiplication Unit (NMU) that can multiply subsets of a vector. The NMU is, to our knowledge, the first arithmetic neural network component that can learn to multiply elements from a vector, when the hidden size is large. The two new components draw inspiration from a theoretical analysis of recently proposed arithmetic components. We find that careful initialization, restricting parameter space, and regularizing for sparsity is important when optimizing the NAU and NMU. Our proposed units NAU and NMU, compared with previous neural units, converge more consistently, have fewer parameters, learn faster, can converge for larger hidden sizes, obtain sparse and meaningful weights, and can extrapolate to negative and small values.
Understanding and Robustifying Differentiable Architecture Search
Authors: Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.  However, DARTS does not work robustly for new problems: we identify a wide range of search spaces for which DARTS yields degenerate architectures with very poor test performance. We study this failure mode and show that, while DARTS successfully minimizes validation loss, the found solutions generalize poorly when they coincide with high validation loss curvature in the  architecture space. We show that by adding one of various types of regularization we can robustify DARTS to find solutions with less curvature and better generalization properties. Based on these observations, we propose several simple variations of DARTS that perform substantially more robustly in practice.  Our observations are robust across five search spaces on three image classification tasks and also hold for the very different domains of disparity estimation (a dense regression task) and language modelling.
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
Authors: Jimmy Ba, Murat Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: This paper investigates the generalization properties of two-layer neural networks in high-dimensions, i.e. when the number of samples $n$, features $d$, and neurons $h$ tend to infinity at the same rate. Specifically, we derive the exact population risk of the unregularized least squares regression problem with two-layer neural networks when either the first or the second layer is trained using a gradient flow under different initialization setups.  When only the second layer coefficients are optimized, we recover the \textit{double descent} phenomenon: a cusp in the population risk appears at $h\approx n$ and further overparameterization decreases the risk. In contrast, when the first layer weights are optimized, we highlight how different scales of initialization lead to different inductive bias, and show that the resulting risk is \textit{independent} of overparameterization. Our theoretical and experimental results suggest that previously studied model setups that provably give rise to \textit{double descent} might not translate to optimizing two-layer neural networks.
Recurrent neural circuits for contour detection
Authors: Drew Linsley*, Junkyung Kim*, Alekh Ashok, Thomas Serre
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: We introduce a deep recurrent neural network architecture that approximates visual cortical circuits (Mély et al., 2018). We show that this architecture, which we refer to as the 𝜸-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting a classic perceptual illusion, known as the orientation-tilt illusion. Correcting this illusion significantly reduces \gnetw contour detection accuracy by driving it to prefer low-level edges over high-level object boundary contours. Overall, our study suggests that the orientation-tilt illusion is a byproduct of neural circuits that help biological visual systems achieve robust and efficient contour detection, and that incorporating these circuits in artificial neural networks can improve computer vision.
Composing Task-Agnostic Policies with Deep Reinforcement Learning
Authors: Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to new problems. In this paper, we propose a novel deep reinforcement learning-based skill transfer and composition method that takes the agent's primitive policies to solve unseen tasks. We evaluate our method in difficult cases where training policy through standard reinforcement learning (RL) or even hierarchical RL is either not feasible or exhibits high sample complexity. We show that our method not only transfers skills to new problem settings but also solves the challenging environments requiring both task planning and motion control with high data efficiency.
Exploring Model-based Planning with Policy Networks
Authors: Tingwu Wang, Jimmy Ba
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Model-based reinforcement learning (MBRL) with model-predictive control or
online planning has shown great potential for locomotion control tasks in both
sample efficiency and asymptotic performance. Despite the successes, the existing
planning methods search from candidate sequences randomly generated in the
action space, which is inefficient in complex high-dimensional environments. In
this paper, we propose a novel MBRL algorithm, model-based policy planning
(POPLIN), that combines policy networks with online planning. More specifically,
we formulate action planning at each time-step as an optimization problem using
neural networks. We experiment with both optimization w.r.t. the action sequences
initialized from the policy network, and also online optimization directly w.r.t. the
parameters of the policy network. We show that POPLIN obtains state-of-the-art
performance in the MuJoCo benchmarking environments, being about 3x more
sample efficient than the state-of-the-art algorithms, such as PETS, TD3 and SAC.
To explain the effectiveness of our algorithm, we show that the optimization surface
in parameter space is smoother than in action space. Further more, we found the
distilled policy network can be effectively applied without the expansive model
predictive control during test time for some environments such as Cheetah. Code
is released.
AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing     
Authors: Xingzhe He, Helen Lu Cao, Bo Zhu
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics.  Our learning architecture jointly defines data in an Eulerian world space, using a static background grid, and a Lagrangian material space, using moving particles. By introducing this Eulerian-Lagrangian representation, we are able to naturally evolve and accumulate particle features using flow velocities generated from a generalized, high-dimensional force field.  We demonstrate the efficacy of this system by solving various point cloud classification and segmentation problems with state-of-the-art performance. The entire geometric reservoir and data flow mimic the pipeline of the classic PIC/FLIP scheme in modeling natural flow, bridging the disciplines of geometric machine learning and physical simulation.
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
Authors: Amartya Sanyal, Philip H. Torr, Puneet K. Dokania
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Exciting new work on generalization bounds for neural networks (NN) given by Bartlett et al. (2017); Neyshabur et al. (2018) closely depend on two parameter- dependant quantities: the Lipschitz constant upper bound and the stable rank (a softer version of rank). Even though these bounds typically have minimal practical utility, they facilitate questions on whether controlling such quantities together could improve the generalization behaviour of NNs in practice. To this end, we propose stable rank normalization (SRN), a novel, provably optimal, and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator. Surprisingly we find that SRN, despite being non-convex, can be shown to have a unique optimal solution. We provide extensive analyses across a wide variety of NNs (DenseNet, WideResNet, ResNet, Alexnet, VGG), where applying SRN to their linear layers leads to improved classification accuracy, while simultaneously showing improvements in genealization, evaluated empirically using—(a) shattering experiments (Zhang et al., 2016); and (b) three measures of sample complexity by Bartlett et al. (2017), Neyshabur et al. (2018), & Wei & Ma. Additionally, we show that, when applied to the discriminator of GANs, it improves Inception, FID, and Neural divergence scores, while learning mappings with low empirical Lipschitz constant.
Few-shot Text Classification with Distributional Signatures
Authors: Yujia Bao, Menghua Wu, Shiyu Chang, Regina Barzilay
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging--lexical features highly informative for one task may be insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (20.0% on average in 1-shot classification).
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Authors: Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.
White Noise Analysis of Neural Networks
Authors: Ali Borji, Sikun Lin
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: A white noise analysis of modern deep neural networks is presented to unveil
their biases at the whole network level or the single neuron level. Our analysis is
based on two popular and related methods in psychophysics and neurophysiology
namely classification images and spike triggered analysis. These methods have
been widely used to understand the underlying mechanisms of sensory systems
in humans and monkeys. We leverage them to investigate the inherent biases of
deep neural networks and to obtain a first-order approximation of their functionality.
We emphasize on CNNs since they are currently the state of the art methods
in computer vision and are a decent model of human visual processing. In
addition, we study multi-layer perceptrons, logistic regression, and recurrent neural
networks. Experiments over four classic datasets, MNIST, Fashion-MNIST,
CIFAR-10, and ImageNet, show that the computed bias maps resemble the target
classes and when used for classification lead to an over two-fold performance than
the chance level. Further, we show that classification images can be used to attack
a black-box classifier and to detect adversarial patch attacks. Finally, we utilize
spike triggered averaging to derive the filters of CNNs and explore how the behavior
of a network changes when neurons in different layers are modulated. Our
effort illustrates a successful example of borrowing from neurosciences to study
ANNs and highlights the importance of cross-fertilization and synergy across machine
learning, deep learning, and computational neuroscience.
Non-Autoregressive Dialog State Tracking
Authors: Hung Le, Richard Socher, Steven C.H. Hoi
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values. However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns. In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level. Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Authors: Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Reinforcement learning (RL) is frequently used to increase performance in text generation tasks,
including machine translation (MT), 
notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). 
However, little is known about what and how these methods learn in the context of MT. 
We prove that one of the most common RL methods for MT does not optimize the 
expected reward, as well as show that other methods take an infeasibly long time to converge.
In fact, our results suggest that RL practices in MT are likely to improve performance
only where the pre-trained parameters are already close to yielding the correct translation.
Our findings further suggest that observed gains may be due to effects unrelated to the training signal, concretely, changes in the shape of the distribution curve.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Authors: Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems,  we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT~\citep{devlin2018bert}. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.
Automated curriculum generation through setter-solver interactions
Authors: Sebastien Racaniere, Andrew Lampinen, Adam Santoro, David Reichert, Vlad Firoiu, Timothy Lillicrap
Published in: International Conference on Learning Representations 2020 (Main Conference Track), 2020

Abstract: Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance.   But in dynamic or sparsely rewarding environments these correlations are often too small,  or rewarding events are too infrequent to make learning feasible. Human education instead relies on curricula –the breakdown of tasks into simpler, static challenges with dense rewards– to build up to complex behaviors.  While curricula are also useful for artificial agents, hand-crafting them is time consuming.  This has lead researchers to explore automatic curriculum generation. Here we explore automatic curriculum generation in rich,dynamic environments.  Using a setter-solver paradigm we show the importance of considering goal validity, goal feasibility, and goal coverage to construct useful curricula.  We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work.  Finally, we demonstrate the value of a novel technique that guides agents towards a desired goal distribution. Altogether, these results represent a substantial step towards applying automatic task curricula to learn complex,  otherwise unlearnable goals,  and to our knowledge are the first to demonstrate automated curriculum generation for goal-conditioned agents in environments where the possible goals vary between episodes.