mtybilly/Instruct2DS huggingface dataset:
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Authors: Kai Xu, Akash Srivastava, Alessio Buccino, Matthias Hennig Cole Hurwitz
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Determining the positions of neurons in an extracellular recording is useful for investigating the functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over heuristic localization methods such as center of mass.
Prior-Free Dynamic Auctions with Low Regret Buyers
Authors: Jon Schneider, Balasubramanian Sivan Yuan Deng
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study the problem of how to repeatedly sell to a buyer running a no-regret, mean-based algorithm. Previous work [Braverman et al., 2018] shows that it is possible to design effective mechanisms in such a setting that extract almost all of the economic surplus, but these mechanisms require the buyer's values each round to be drawn independently and identically from a fixed distribution. In this work, we do away with this assumption and consider the prior-free setting where the buyer's value each round is chosen adversarially (possibly adaptively). 

We show that even in this prior-free setting, it is possible to extract a $(1-\varepsilon)$-approximation of the full economic surplus for any $\varepsilon > 0$. The number of options offered to a buyer in any round scales independently of the number of rounds $T$ and polynomially in $\varepsilon$. We show that this is optimal up to a polynomial factor; any mechanism achieving this approximation factor, even when values are drawn stochastically, requires at least $\Omega(1/\varepsilon)$ options.

Finally, we examine what is possible when we constrain our mechanism to a natural auction format where overbidding is dominated. Braverman et al. [2018] show that even when values are drawn from a known stochastic distribution supported on $[1/H, 1]$, it is impossible in general to extract more than $O(\log\log H / \log H)$ of the economic surplus. We show how to achieve the same approximation factor in the prior-independent setting (where the distribution is unknown to the seller), and an approximation factor of $O(1 / \log H)$ in the prior-free setting (where the values are chosen adversarially).
When does label smoothing help?
Authors: Simon Kornblith, Geoffrey E. Hinton Rafael Müller
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective.  To explain these observations, we visualize how label smoothing changes the  representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.
A General Framework for Symmetric Property Estimation
Authors: Kirankumar Shiragur, Aaron Sidford Moses Charikar
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: In this paper we provide a general framework for estimating symmetric properties of distributions from i.i.d. samples. For a broad class of symmetric properties we identify the  {\em easy} region where empirical estimation works and the {\em difficult} region where more complex estimators are required. We show that by approximately computing the profile maximum likelihood (PML) distribution \cite{ADOS16} in this difficult region we obtain a symmetric property estimation framework that is sample complexity optimal for many properties in a broader parameter regime than previous universal estimation approaches based on PML. The resulting algorithms based on these \emph{pseudo PML distributions} are also more practical.
Deep Generative Video Compression
Authors: JUN HAN, Christopher Schroers, Stephan Mandt Salvator Lombardo
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The usage of deep generative models for image compression has led to impressive
performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this representation according to predictions of the sequential VAE.  Rate-distortion evaluations on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved when training the model on specialized content.
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Authors: Gabriel Bender, Quoc V Le, Jiquan Ngiam Brandon Yang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should
be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels
for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural
network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv.
Towards a Zero-One Law for Column Subset Selection
Authors: David Woodruff, Peilin Zhong Zhao Song
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-$k$ matrix $B$ minimizing the sum of absolute values of differences to a given $n$-by-$n$ matrix $A$, $\min_{\textrm{rank-}k~B}\|A-B\|_1$, or more generally finding a rank-$k$ matrix $B$ which minimizes the sum of $p$-th powers of absolute values of differences, $\min_{\textrm{rank-}k~B}\|A-B\|_p^p$. Many of these algorithms are linear time columns subset selection algorithms, 
returning a subset of $\poly(k \log n)$ columns whose cost is no more than a $\poly(k)$ factor larger than the cost of the best rank-$k$ matrix. 
The above error measures are special cases of the following general entrywise
low rank approximation problem: given an arbitrary function $g:\mathbb{R} \rightarrow \mathbb{R}_{\geq 0}$, find a rank-$k$ matrix $B$ which minimizes $\|A-B\|_g = \sum_{i,j}g(A_{i,j}-B_{i,j})$. A natural question is which functions $g$ admit efficient approximation algorithms? Indeed, this is a central question of recent work studying generalized low rank models. In this work we give approximation algorithms for {\it every} function $g$ which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary. Further, our algorithm is efficient if the function $g$ admits an efficient approximate regression algorithm. Our approximation algorithms handle functions which are not even scale-invariant, such as the Huber loss function, which we show have very different structural properties than $\ell_p$-norms, e.g., one can show the lack of scale-invariance causes any column subset selection algorithm to provably require a $\sqrt{\log n}$ factor larger number of columns than $\ell_p$-norms; nevertheless we design the first efficient column subset selection algorithms for such error measures.
Neural Attribution for Semantic Bug-Localization in Student Programs
Authors: Aditya Kanade, Shirish Shevade Rahul Gupta
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs' functional correctness. They return failing tests to the students as feedback. However, students may find it difficult to debug their programs if they receive no hints about where the bug is and how to fix it. In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program. At the heart of our technique is a novel tree convolutional neural network which is trained to predict whether a program passes or fails a given test. To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes. Our experiments show that NeuralBugLocator is generally more accurate than two state-of-the-art program-spectrum based and one syntactic difference based bug-localization baselines.
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Authors: Ludovic DOS SANTOS, Kevin Scaman Igor Colin
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the computation is distributed per layer instead of per example. For smooth convex and non-convex objective functions, we provide matching lower and upper complexity bounds and show that a naive pipeline parallelization of Nesterov's accelerated gradient descent is optimal. For non-smooth convex functions, we provide a novel algorithm coined Pipeline Parallel Random Smoothing (PPRS) that is within a $d^{1/4}$ multiplicative factor of the optimal convergence rate, where $d$ is the underlying dimension. While the convergence rate still obeys a slow $\varepsilon^{-2}$ convergence rate, the depth-dependent part is accelerated, resulting in a near-linear speed-up and convergence time that only slightly depends on the depth of the deep learning architecture. Finally, we perform an empirical analysis of the non-smooth non-convex case and show that, for difficult and highly non-smooth problems, PPRS outperforms more traditional optimization algorithms such as gradient descent and Nesterov's accelerated gradient descent for problems where the sample size is limited, such as few-shot or adversarial learning.
DppNet: Approximating Determinantal Point Processes with Deep Networks
Authors: Yaniv Ovadia, Jasper Snoek Zelda E. Mariet
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Determinantal point processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets.  We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors.  We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPPs so powerful and unique. Empirically, we show across multiple datasets that DPPNET is orders of magnitude faster than competing approaches for DPP sampling, while generating high-likelihood samples and performing as well as DPPs on downstream tasks.
Nonzero-sum Adversarial Hypothesis Testing Games
Authors: Patrick Loiseau Sarath Yasodharan
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rates of convergence of classification errors at equilibrium, which are analogous to the well-known Chernoff-Stein lemma and Chernoff information that describe the error exponents in the classical binary hypothesis testing problem, but with parameters derived from the adversarial model. The results are validated through numerical experiments.
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Authors: guiguang ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu Xiaohan Ding
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning is an approach for deep model compression, which aims at eliminating some parameters with tolerable performance degradation. In this paper, we propose a novel momentum-SGD-based optimization method to reduce the network complexity by on-the-fly pruning. Concretely, given a global compression ratio, we categorize all the parameters into two parts at each training iteration which are updated using different rules. In this way, we gradually zero out the redundant parameters, as we update them using only the ordinary weight decay but no gradients derived from the objective function. As a departure from prior methods that require heavy human works to tune the layer-wise sparsity ratios, prune by solving complicated non-differentiable problems or finetune the model after pruning, our method is characterized by 1) global compression that automatically finds the appropriate per-layer sparsity ratios; 2) end-to-end training; 3) no need for a time-consuming re-training process after pruning; and  4) superior capability to find better winning tickets which have won the initialization lottery.
Thompson Sampling and Approximate Inference
Authors: Yasin Abbasi Yadkori, Justin Domke My Phan
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study the effects of approximate inference on the performance of Thompson sampling in the $k$-armed bandit problems. Thompson sampling is a successful algorithm for online decision-making but requires posterior inference, which often must be approximated in practice. We show that even small constant inference error (in $\alpha$-divergence) can lead to poor performance (linear regret) due to under-exploration (for $\alpha<1$) or over-exploration (for $\alpha>0$) by the approximation. While for $\alpha > 0$ this is unavoidable, for $\alpha \leq 0$ the regret can be improved by adding a small amount of forced exploration even when the inference error is a large constant.
Quantum Wasserstein Generative Adversarial Networks
Authors: Huang Yiming, Tongyang Li, Soheil Feizi, Xiaodi Wu Shouvanik Chakrabarti
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classical and quantum generative models,  we propose the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative models even on noisy quantum hardware.  Specifically, we propose a definition of the Wasserstein semimetric between quantum data, which inherits a few key theoretical merits of its classical counterpart. We also demonstrate how to turn the quantum Wasserstein semimetric into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. As a surprising application, our quantum WGAN has been used to generate a 3-qubit quantum circuit of ~50 gates that well approximates a 3-qubit 1-d Hamiltonian simulation circuit that requires over 10k gates using standard techniques.
Deep Learning without Weight Transport
Authors: Collin Wilson, Peter Humphreys, Timothy Lillicrap, Douglas B Tweed Mohamed Akrout
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Current algorithms for deep learning probably cannot run in the brain because
they rely on weight transport, where forward-path neurons transmit their synaptic
weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transport by using random feedback weights, but it performs poorly on hard visual-recognition tasks. Here we describe two mechanisms — a neural circuit called a weight mirror and a modification of an algorithm proposed by Kolen and Pollack in 1994 — both of which let the feedback path learn appropriate synaptic weights quickly and accurately even in large networks, without weight transport or complex wiring. Tested on the ImageNet visual-recognition task, these mechanisms outperform both feedback alignment and the newer sign-symmetry method, and nearly match backprop, the standard algorithm of deep learning, which uses weight transport.
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
Authors: Francis Bach, Simon Lacoste-Julien Gauthier Gidel
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequently, this choice can be considered as an implicit regularization for the training of over-parametrized models. In this work, we push this idea further by studying the discrete gradient dynamics of the training of a two-layer linear network with the least-squares loss. Using a time rescaling, we show that, with a vanishing initialization and a small enough step size, this dynamics sequentially learns the solutions of a reduced-rank regression with a gradually increasing rank.
Generative Models for Graph-Based Protein Design
Authors: Vikas Garg, Regina Barzilay, Tommi Jaakkola John Ingraham
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. We develop relational language models for protein sequences that directly condition on a graph specification of the  target structure. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
Authors: Peng Li Wenrui Zhang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient event-driven hardware neuromorphic processors. As an important class of SNNs,  recurrent spiking neural networks (RSNNs) possess great computational power. However, the practical application of RSNNs is severely limited by challenges in training.  Biologically-inspired unsupervised learning has limited capability in boosting the performance of RSNNs. On the other hand,  existing backpropagation (BP) methods suffer from high complexity of unrolling in time, vanishing and exploding gradients, and approximate differentiation of discontinuous spiking activities when applied to RSNNs.  To enable supervised training of RSNNs under a well-defined loss function,  we present a novel Spike-Train level RSNNs Backpropagation (ST-RSBP) algorithm for training deep RSNNs. The proposed ST-RSBP directly computes the gradient of a rated-coded loss function defined at the output layer of the network w.r.t tunable parameters. The scalability of ST-RSBP is achieved by the proposed spike-train level computation during which temporal effects of the SNN is captured in both the forward and backward pass of BP. Our ST-RSBP algorithm can be broadly applied to RSNNs with a single recurrent layer or deep RSNNs with multiple feed-forward and recurrent layers.  Based upon challenging speech and image datasets including TI46, N-TIDIGITS, Fashion-MNIST and MNIST,  ST-RSBP is able to train RSNNs with an accuracy surpassing that of the current state-of-art SNN BP algorithms and conventional non-spiking deep learning models.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Authors: Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu Derek Yang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution. Existing distributional RL algorithms parameterize either the probability side or the return value side of the distribution function, leaving the other side uniformly fixed as in C51, QR-DQN or randomly sampled as in IQN. In this paper, we propose fully parameterized quantile function that parameterizes both the quantile fraction axis (i.e., the x-axis) and the value axis (i.e., y-axis) for distributional RL. Our algorithm contains a fraction proposal network that generates a discrete set of quantile fractions and a quantile value network that gives corresponding quantile values. The two networks are jointly trained to find the best approximation of the true distribution. Experiments on 55 Atari Games show that our algorithm significantly outperforms existing distributional RL algorithms and creates a new record for the Atari Learning Environment for non-distributed agents.
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Authors: Michael Tarr, Leila Wehbe Aria Wang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Convolutional neural networks (CNNs) trained for object classification have been widely used to account for visually-driven neural responses in both human and primate brains. However, because of the generality and complexity of object classification, despite the effectiveness of CNNs in predicting brain activity, it is difficult to draw specific inferences about neural information processing using CNN-derived representations. To address this problem, we used learned representations drawn from 21 computer vision tasks to construct encoding models for predicting brain responses from BOLD5000---a large-scale dataset comprised of fMRI scans collected while observers viewed over 5000 naturalistic scene and object images. Encoding models based on task features predict activity in different regions across the whole brain. Features from 3D tasks such as keypoint/edge detection explain greater variance compared to 2D tasks---a pattern observed across the whole brain. Using results across all 21 task representations, we constructed a ``task graph’’ based on the spatial layout of well-predicted brain areas from each task. A comparison of this brain-derived task structure to the task structure derived from transfer learning accuracy demonstrate that tasks with higher transferability make similar predictions for brain responses from different regions. These results---arising out of state-of-the-art computer vision methods---help reveal the task-specific architecture of the human visual system.
Adaptive Gradient-Based Meta-Learning Methods
Authors: Maria-Florina F Balcan, Ameet S Talwalkar Mikhail Khodak
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms. Our approach enables the task-similarity to be learned adaptively, provides sharper transfer-risk bounds in the setting of statistical learning-to-learn, and leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure. We use our theory to modify several popular meta-learning algorithms and improve their training and meta-test-time performance on standard problems in few-shot and federated learning.
Compositional generalization through meta sequence-to-sequence learning
Authors: Brenden M Lake
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: People can learn a new concept and use it compositionally, understanding how to "blicket twice" after learning how to "blicket." In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems. Meta se2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.
Meta-Learning Representations for Continual Learning
Authors: Martha White Khurram Javed
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The reviews had two major concerns: lack of a benchmarking on a complex dataset, and unclear writing. To address these two major issues we: 
1- Rewrote experiments section with improved terminology to make the paper more clear. Previously we were using the term Pretraining to refer to both a baseline and the meta-training stage. As the reviewers pointed out, this was confusing. We have replaced one of the usages with 'meta-training.' We have also changed evaluation to meta-testing. 
2- Added mini-imagenet experiments to show that the proposed method scales to more complex datasets. 
Moreover, it wasn't clear if the objective we introduced improved over a maml like objective that also learned representations. We added MAML-Rep as a baseline that shows that our method -- which minimizes interference in addition to maximizing fast adaptation -- performs noticeably better. 
We also added the pseudo-code of the algorithms to the main paper as requested by reviewers. Moreover, we contrast our algorithm with MAML to highlight the difference between the two. We believe that this makes the current version significantly more clear to anyone who already understands the MAML objective. 
We have fixed various minor issues in writing and included some missing related work. (bengio2019meta, nagabandi19, al2017continuous) that we have discovered since our initial submission. 
Finally, we thank the reviewers and the meta-reviewer for the feedback, which allowed us to improve the work in several aspects.
Massively scalable Sinkhorn distances via the Nyström method
Authors: Francis Bach, Alessandro Rudi, Jonathan Niles-Weed Jason Altschuler
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The Sinkhorn "distance," a variant of the Wasserstein distance with entropic regularization, is an increasingly popular tool in machine learning and statistical inference. However, the time and memory requirements of standard algorithms for computing this distance grow quadratically with the size of the data, rendering them prohibitively expensive on massive data sets. In this work, we show that this challenge is surprisingly easy to circumvent: combining two simple techniques—the Nyström method and Sinkhorn scaling—provably yields an accurate approximation of the Sinkhorn distance with significantly lower time and memory requirements than other approaches. We prove our results via new, explicit analyses of the Nyström method and of the stability properties of Sinkhorn scaling. We validate our claims experimentally by showing that our approach easily computes Sinkhorn distances on data sets hundreds of times larger than can be handled by other techniques.
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Authors: Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao Ming Hou
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Tensor-based multimodal fusion techniques have exhibited great predictive performance. However, one limitation is that existing approaches only consider bilinear or trilinear pooling, which fails to unleash the complete expressive power of multilinear fusion with restricted orders of interactions. More importantly, simply fusing features all at once ignores the complex local intercorrelations, leading to the deterioration of prediction. In this work, we first propose a polynomial tensor pooling (PTP) block for integrating multimodal features by considering high-order moments, followed by a tensorized fully connected layer. Treating PTP as a building block, we further establish a hierarchical polynomial fusion network (HPFN) to recursively transmit local correlations into global ones. By stacking multiple PTPs, the expressivity capacity of HPFN enjoys an exponential growth w.r.t. the number of layers, which is shown by the equivalence to a very deep convolutional arithmetic circuits. Various experiments demonstrate that it can achieve the state-of-the-art performance.
A Composable Specification Language for Reinforcement Learning Tasks
Authors: Rajeev Alur, Osbert Bastani Kishor Jothimurugan
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Reinforcement learning is a promising approach for learning control policies for robot tasks. However, specifying complex tasks (e.g., with multiple objectives and safety constraints) can be challenging, since the user must design a reward function that encodes the entire task. Furthermore, the user often needs to manually shape the reward to ensure convergence of the learning algorithm. We propose a language for specifying complex control tasks, along with an algorithm that compiles specifications in our language into a reward function and automatically performs reward shaping. We implement our approach in a tool called SPECTRL, and show that it outperforms several state-of-the-art baselines.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Authors: Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler Wenzheng Chen
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present DIB-Render, a novel rendering framework through which gradients can be analytically computed. Key to our approach is to view rasterization as a weighted interpolation, allowing image gradients to back-propagate through various standard vertex shaders within a single framework. Our approach supports optimizing over vertex positions, colors, normals, light directions and texture coordinates, and allows us to incorporate various well-known lighting models from graphics. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively 2D supervision.
On the Utility of Learning about Humans for Human-AI Coordination
Authors: Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, Anca Dragan Micah Carroll
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Agents that assume their partner to be optimal or similar to them can converge to coordination protocols that fail to understand and be understood by humans. To demonstrate this, we introduce a simple environment that requires challenging coordination, based on the popular game Overcooked, and learn a simple model that mimics human play. We evaluate the performance of agents trained via self-play and population-based training. These agents perform very well when paired with themselves, but when paired with our human model, they are significantly worse than agents designed to play with the human model. An experiment with a planning algorithm yields the same conclusion, though only when the human-aware planner is given the exact human model that it is playing with. A user study with real humans shows this pattern as well, though less strongly. Qualitatively, we find that the gains come from having the agent adapt to the human's gameplay. Given this result, we suggest several approaches for designing agents that learn about humans in order to better coordinate with them. Code is available at https://github.com/HumanCompatibleAI/overcooked_ai.
FastSpeech: Fast, Robust and Controllable Text to Speech
Authors: Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu Yi Ren
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram using vocoder such as WaveNet. Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i.e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control). In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS. Specifically, we extract attention alignments from an encoder-decoder based teacher model for phoneme duration prediction, which is used by a length regulator to expand the source phoneme sequence to match the length of the target mel-spectrogram sequence for parallel mel-spectrogram generation. Experiments on the LJSpeech dataset show that our parallel model matches autoregressive models in terms of speech quality, nearly eliminates the problem of word skipping and repeating in particularly hard cases, and can adjust voice speed smoothly. Most importantly, compared with autoregressive Transformer TTS, our model speeds up mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x. Therefore, we call our model FastSpeech.
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
Authors: Matthew Walter Falcon Dai
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose a new complexity measure for Markov decision processes (MDPs), the maximum expected hitting cost (MEHC). This measure tightens the closely related notion of diameter [JOA10] by accounting for the reward structure.
We show that this parameter replaces diameter in the upper bound on the optimal value span of an extended MDP, thus refining the associated upper bounds on the regret of several UCRL2-like algorithms.
Furthermore, we show that potential-based reward shaping [NHR99] can induce equivalent reward functions with varying informativeness, as measured by MEHC.
By analyzing the change in the maximum expected hitting cost, this work presents a formal understanding of the effect of potential-based reward shaping on regret (and sample complexity) in the undiscounted average reward setting.
We further establish that shaping can reduce or increase MEHC by at most a factor of two in a large class of MDPs with finite MEHC and unsaturated optimal average rewards.
Park: An Open Platform for Learning-Augmented Computer Systems
Authors: Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, ravichandra addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Dr.Mohammad Alizadeh Hongzi Mao
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present Park, a platform for researchers to experiment with Reinforcement Learning (RL)  for computer systems. Using RL for improving the performance of systems has a lot of potential, but  is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then  propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world  system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.
Adaptive Influence Maximization with Myopic Feedback
Authors: Wei Chen Binghui Peng
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study the adaptive influence maximization problem with myopic feedback under the independent cascade model: one sequentially selects k nodes as seeds one by one from a social network, and each selected seed returns the immediate neighbors it activates as the feedback available for by later selections, and the goal is to maximize the expected number of total activated nodes, referred as the influence spread. We show that the adaptivity gap, the ratio between the optimal adaptive influence spread and the optimal non-adaptive influence spread, is at most 4 and at least e/(e-1), and the approximation ratios with respect to the optimal adaptive influence spread of both the non-adaptive greedy and adaptive greedy algorithms are at least \frac{1}{4}(1 - \frac{1}{e}) and at most \frac{e^2 + 1}{(e + 1)^2} < 1 - \frac{1}{e}. Moreover, the approximation ratio of the non-adaptive greedy algorithm is no worse than that of the adaptive greedy algorithm, when considering all graphs.
Our result confirms a long-standing open conjecture of Golovin and Krause (2011) on the constant approximation ratio of adaptive greedy with myopic feedback, and it also suggests that adaptive greedy may not bring much benefit under myopic feedback.
Compression with Flows via Local Bits-Back Coding
Authors: Evan Lohn, Pieter Abbeel Jonathan Ho
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood. However, there is no guaranteed existence of computationally efficient codes that achieve these lengths, and coding algorithms must be hand-tailored to specific types of generative models to ensure computational efficiency. Such coding algorithms are known for autoregressive models and variational autoencoders, but not for general types of flow models. To fill in this gap, we introduce local bits-back coding, a new compression technique for flow models. We present efficient algorithms that instantiate our technique for many popular types of flows, and we demonstrate that our algorithms closely achieve theoretical codelengths for state-of-the-art flow models on high-dimensional data.
On Adversarial Mixup Resynthesis
Authors: Sina Honari, Vikas Verma, Alex M Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Chris Pal Christopher Beckham
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Authors: Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V Le, Honglak Lee Ruben Villegas
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: finding minimal inductive bias for video prediction while maximizing network capacity. We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.
Variational Bayes under Model Misspecification
Authors: David Blei Yixin Wang
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees, most of which focus on well-specified models. However, models are rarely well-specified in practice. In this work, we study VB under model misspecification. We prove the VB posterior is asymptotically normal and centers at the value that minimizes the Kullback-Leibler (KL) divergence to the true data-generating distribution. Moreover, the VB posterior mean centers at the same value and is also asymptotically normal. These results generalize the variational Bernstein--von Mises theorem [29] to misspecified models. As a consequence of these results, we find that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions. It explains the widely observed phenomenon that VB achieves comparable predictive accuracy with MCMC even though VB uses an approximating family. As illustrations, we study VB under three forms of model misspecification, ranging from model over-/under-dispersion to latent dimensionality misspecification. We conduct two simulation studies that demonstrate the theoretical results.