mtybilly/Instruct2DS huggingface dataset:
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
Authors: Tao Tu, John Paisley, Stefan Haufe, Paul Sajda
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Inferring effective connectivity between spatially segregated brain regions is important for understanding human brain dynamics in health and disease. Non-invasive neuroimaging modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are often used to make measurements and infer connectivity. However most studies do not consider integrating the two modalities even though each is an indirect measure of the latent neural dynamics and each has its own spatial and/or temporal limitations. In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data. Our method first identifies task-dependent and subject-dependent regions of interest (ROI) based on the analysis of fMRI data. Directed influences between the latent neural states at these ROIs are then modeled as a multivariate autogressive (MVAR) process driven by various exogenous inputs. The latent neural dynamics give rise to the observed scalp EEG measurements via a biophysically informed linear EEG forward model. We use a mean-field variational Bayesian approach to infer the posterior distribution of latent states and model parameters. The performance of the model was evaluated on two sets of simulations. Our results emphasize the importance of obtaining accurate spatial localization of ROIs from fMRI. Finally, we applied the model to simultaneously recorded EEG-fMRI data from 10 subjects during a Face-Car-House visual categorization task and compared the change in connectivity induced by different stimulus categories.
A Solvable High-Dimensional Model of GAN
Authors: Chuang Wang, Hong Hu, Yue Lu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measuring the quality of the training process converge to a deterministic process characterized by an ordinary differential equation (ODE), whereas the microscopic states containing all the detailed weights remain stochastic, whose dynamics can be described by a stochastic differential equation (SDE). This analysis provides a new perspective different from recent analyses in the limit of small learning rate, where the microscopic state is always considered deterministic, and the contribution of noise is ignored. From our analysis, we show that the level of the background noise is essential to the convergence of the training process: setting the noise level too strong leads to failure of feature recovery, whereas setting the noise too weak causes oscillation.  Although this work focuses on a simple copy model of GAN, we believe the analysis methods and insights developed here would prove useful in the theoretical understanding of other variants of GANs with more advanced training algorithms.
On The Classification-Distortion-Perception Tradeoff
Authors: Dong Liu, Haochen Zhang, Zhiwei Xiong
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Signal degradation is ubiquitous, and computational restoration of degraded signal has been investigated for many years. Recently, it is reported that the capability of signal restoration is fundamentally limited by the so-called perception-distortion tradeoff, i.e. the distortion and the perceptual difference between the restored signal and the ideal "original" signal cannot be made both minimal simultaneously. Distortion corresponds to signal fidelity and perceptual difference corresponds to perceptual naturalness, both of which are important metrics in practice. Besides, there is another dimension worthy of consideration--the semantic quality of the restored signal, i.e. the utility of the signal for recognition purpose. In this paper, we extend the previous perception-distortion tradeoff to the case of classification-distortion-perception (CDP) tradeoff, where we introduced the classification error rate of the restored signal in addition to distortion and perceptual difference. In particular, we consider the classification error rate achieved on the restored signal using a predefined classifier as a representative metric for semantic quality. We rigorously prove the existence of the CDP tradeoff, i.e. the distortion, perceptual difference, and classification error rate cannot be made all minimal simultaneously. We also provide both simulation and experimental results to showcase the CDP tradeoff. Our findings can be useful especially for computer vision research where some low-level vision tasks (signal restoration) serve for high-level vision tasks (visual understanding). Our code and models have been published.
Variance Reduction for Matrix Games
Authors: Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present a randomized primal-dual algorithm that solves the problem minx maxy y^T A x to additive error epsilon in time nnz(A) + sqrt{nnz(A) n} / epsilon, for matrix A with larger dimension n and nnz(A) nonzero entries. This improves the best known exact gradient methods by a factor of sqrt{nnz(A) / n} and is faster than fully stochastic gradient methods in the accurate and/or sparse regime epsilon < sqrt{n / nnz(A)$. Our results hold for x,y in the simplex (matrix games, linear programming) and for x in an \ell_2 ball and y in the simplex (perceptron / SVM, minimum enclosing ball). Our algorithm combines the Nemirovski's "conceptual prox-method" and a novel reduced-variance gradient estimator based on "sampling from the difference" between the current iterate and a reference point.
Efficient Forward Architecture Search
Authors: Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric J Horvitz, Debadeepta Dey
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively 
add shortcut connections to existing network layers. The added shortcut connections 
effectively perform gradient boosting on the augmented layers.
The proposed algorithm is motivated by the feature selection algorithm 
forward stage-wise linear regression, since we consider NAS as a generalization 
of feature selection for regression, where NAS selects shortcuts among layers 
instead of selecting features. 
In order to reduce the number of trials of possible connection combinations, we train
jointly all possible connections at each stage of growth while leveraging
feature selection techniques to choose a subset of them. 
We experimentally show this process to be an efficient forward 
architecture search algorithm that can find competitive 
models using few GPU days in both the search space of repeatable 
network modules (cell-search) and the space of general networks (macro-search). 
Petridish is particularly well-suited for warm-starting from existing models 
crucial for lifelong-learning scenarios.
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Authors: Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images. In this paper, we propose a framework named E^3Outlier, which can perform UOD in a both effective and end-to-end manner: First, instead of the commonly-used autoencoders in previous end-to-end UOD methods, E^3Outlier for the first time leverages a discriminative DNN for better representation learning, by using surrogate supervision to create multiple pseudo classes from original unlabelled data. Next, unlike classic UOD that utilizes data characteristics like density or proximity, we exploit a novel property named inlier priority to enable end-to-end UOD by discriminative DNN. We demonstrate theoretically and empirically that the intrinsic class imbalance of inliers/outliers will make the network prioritize minimizing inliers' loss when inliers/outliers are indiscriminately fed into the network for training, which enables us to differentiate outliers directly from DNN's outputs. Finally, based on inlier priority, we propose the negative entropy based score as a simple and effective outlierness measure. Extensive evaluations show that E^3Outlier significantly advances UOD performance by up to 30% AUROC against state-of-the-art counterparts, especially on relatively difficult benchmarks.
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
Authors: Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games. In this class, players control the inputs of a smooth function whose output is being applied to a bilinear zero-sum game. This class of games is motivated by the  indirect nature of the competition in
Generative Adversarial Networks, where players control the parameters of a neural network while the actual competition happens between the distributions that the generator and discriminator capture. We establish theoretically, that depending on the specific instance of the problem gradient-descent-ascent dynamics  can exhibit a variety of behaviors antithetical to convergence to the game theoretically meaningful min-max solution. Specifically, different forms of recurrent behavior (including periodicity and Poincar\'{e} recurrence) are possible as well as convergence to spurious (non-min-max) equilibria for a positive measure of initial conditions. At the technical level, our analysis combines tools from optimization theory, game theory and dynamical systems.
End-to-End Learning on 3D Protein Structure for Interface Prediction
Authors: Raphael Townshend, Rishi Bedi, Patricia Suriana, Ron Dror
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited.  Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question.  To address this question, we focused on a central problem in biology: predicting how proteins interact with one another—that is, which surfaces of one protein bind to those of another protein.  We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously.  We found that these biases significantly degrade the performance of existing methods on gold-standard data.  Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning model for protein interface prediction, the Siamese Atomic Surfacelet Network (SASNet).  Using only spatial coordinates and identities of atoms, SASNet outperforms state-of-the-art methods trained on gold-standard structural data, even when trained on only 3% of our new dataset.  Code and data available at https://github.com/drorlab/DIPS.
Scalable Global Optimization via Local Bayesian Optimization
Authors: David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, Matthias Poloczek
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition. This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems. We propose the TuRBO algorithm that fits a collection of local models and performs a principled global allocation of samples across these models via an implicit bandit approach. A comprehensive evaluation demonstrates that TuRBO outperforms state-of-the-art methods from machine learning and operations research on problems spanning reinforcement learning, robotics, and the natural sciences.
Positional Normalization
Authors: Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: A widely deployed method for reducing the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics.  In this paper, we propose a novel normalization method that deviates from this theme. Our approach, which we refer to as Positional Normalization (PONO), normalizes exclusively across channels, which allows us to capture structural information of the input image in the first and second moments. Instead of disregarding this information, we inject it into later layers to preserve or transfer structural information in generative networks. We show that PONO significantly improves the performance of deep networks across a wide range of model architectures and image generation tasks.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Authors: Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis
Authors: Yingying Li, Xin Chen, Na Li
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: This paper studies the online optimal control problem with time-varying convex stage costs for a time-invariant linear dynamical system, where a finite lookahead window of accurate predictions of the stage costs are available at each time. We design online algorithms, Receding Horizon Gradient-based Control (RHGC), that utilize the predictions through finite steps of gradient computations. We study the algorithm performance measured by dynamic regret: the online performance minus the optimal performance in hindsight. It is shown that the dynamic regret of RHGC decays exponentially with the size of the lookahead window. In addition, we provide a fundamental limit of the dynamic regret for any online algorithms by considering linear quadratic tracking problems. The regret upper bound of one RHGC method almost reaches the fundamental limit, demonstrating the effectiveness of the algorithm. Finally, we numerically test our algorithms for both linear and nonlinear systems to show the effectiveness and generality of our RHGC.
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Authors: Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Learning useful representations is a key ingredient to the success of modern machine learning. Currently, representation learning mostly relies on embedding data into Euclidean space. However, recent work has shown that data in some domains is better modeled by non-euclidean metric spaces, and inappropriate geometry can result in inferior performance. In this paper, we aim to eliminate the inductive bias imposed by the embedding space geometry. Namely, we propose to map data into more general non-vector metric spaces: a weighted graph with a shortest path distance. By design, such graphs can model arbitrary geometry with a proper configuration of edges and weights. Our main contribution is PRODIGE: a method that learns a weighted graph representation of data end-to-end by gradient descent. Greater generality and fewer model assumptions make PRODIGE  more powerful than existing embedding-based approaches. We confirm the superiority of our method via extensive experiments on a wide range of tasks, including classification, compression, and collaborative filtering.
Gradient Information for Representation and Modeling
Authors: Jie Ding, Robert Calderbank, Vahid Tarokh
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information.  These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc.  in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability and robustness.  As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Authors: Qiming ZHANG, Jing Zhang, Wei Liu, Dacheng Tao
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. UDA is of particular significance since no extra effort is devoted to annotating target domain samples. However, the different data distributions in the two domains, or \emph{domain shift/discrepancy}, inevitably compromise the UDA performance. Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category-agnostic feature alignment. In this paper, we propose a novel category anchor-guided (CAG) UDA model for semantic segmentation, which explicitly enforces category-aware feature alignment to learn shared discriminative features and classifiers simultaneously. First, the category-wise centroids of the source domain features are used as guided anchors to identify the active features in the target domain and also assign them pseudo-labels. Then, we leverage an anchor-based pixel-level distance loss and a discriminative loss to drive the intra-category features closer and the inter-category features further apart, respectively. Finally, we devise a stagewise training mechanism to reduce the error accumulation and adapt the proposed model progressively. Experiments on both the GTA5$\rightarrow $Cityscapes and SYNTHIA$\rightarrow $Cityscapes scenarios demonstrate the superiority of our CAG-UDA model over the state-of-the-art methods. The code is available at \url{https://github.com/RogerZhangzz/CAG\_UDA}.
Novel positional encodings to enable tree-based transformers
Authors: Vighnesh Shiv, Chris Quirk
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis.
On sequence-structured data, transformers have been shown to learn relationships across arbitrary pairs of positions more reliably than recurrent models.
Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-to-tree, tree-to-sequence, and tree-to-tree mappings.
Our approach abstracts the transformer's sinusoidal positional encodings, allowing us to instead use a novel positional encoding scheme to represent node positions within trees.
We evaluated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over both sequence-to-sequence transformers and state-of-the-art tree-based LSTMs on several datasets.
In particular, our results include a 22% absolute increase in accuracy on a JavaScript to CoffeeScript translation dataset.
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks
Authors: Spencer Frei, Yuan Cao, Quanquan Gu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased generalization and stability of networks with this architecture, although there have been limited theoretical guarantees for this improved performance.   In this work, we analyze overparameterized deep residual networks trained by gradient descent following random initialization, and demonstrate that (i) the class of networks learned by gradient descent constitutes a small subset of the entire neural network function class, and (ii) this subclass of networks is sufficiently large to guarantee small training error.  By showing (i) we are able to demonstrate that deep residual networks trained with gradient descent have a small generalization gap between training and test error, and together with (ii) this guarantees that the test error will be small. Our optimization and generalization guarantees require overparameterization that is only logarithmic in the depth of the network, which helps explain why residual networks are preferable to fully connected ones.
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Authors: Hongteng Xu, Dixin Luo, Lawrence Carin
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a novel and theoretically-supported paradigm for large-scale graph analysis.
The proposed method is based on the fact that Gromov-Wasserstein discrepancy is a pseudometric on graphs. 
Given two graphs, the optimal transport associated with their Gromov-Wasserstein discrepancy provides the correspondence between their nodes and achieves graph matching. 
When one of the graphs is a predefined graph with isolated but self-connected nodes ($i.e.$, disconnected graph), the optimal transport indicates the clustering structure of the other graph and achieves graph partitioning. 
Further, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs. 
Our method combines a recursive $K$-partition mechanism with a warm-start proximal gradient algorithm, whose time complexity is $\mathcal{O}(K(E+V)\log_K V)$ for graphs with $V$ nodes and $E$ edges. 
To our knowledge, our method is the first attempt to make Gromov-Wasserstein discrepancy applicable to large-scale graph analysis and unify graph partitioning and matching into the same framework.
It outperforms state-of-the-art graph partitioning and matching methods, achieving a trade-off between accuracy and efficiency.
Riemannian batch normalization for SPD neural networks
Authors: Daniel Brooks, Olivier Schwander, Frederic Barbaresco, Jean-Yves Schneider, Matthieu Cord
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Covariance matrices have attracted attention for machine learning applications due
to their capacity to capture interesting structure in the data. The main challenge
is that one needs to take into account the particular geometry of the Riemannian
manifold of symmetric positive definite (SPD) matrices they belong to. In the con-
text of deep networks, several architectures for these matrices have recently been
proposed. In our article, we introduce a Riemannian batch normalization (batch-
norm) algorithm, which generalizes the one used in Euclidean nets. This novel
layer makes use of geometric operations on the manifold, notably the Riemannian
barycenter, parallel transport and non-linear structured matrix transformations. We
derive a new manifold-constrained gradient descent algorithm working in the space
of SPD matrices, allowing to learn the batchnorm layer. We validate our proposed
approach with experiments in three different contexts on diverse data types: a
drone recognition dataset from radar observations, and on emotion and action
recognition datasets from video and motion capture data. Experiments show that
the Riemannian batchnorm systematically gives better classification performance
compared with leading methods and a remarkable robustness to lack of data.
Deep Set Prediction Networks
Authors: Yan Zhang, Jonathon Hare, Adam Prugel-Bennett
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.
A unified theory for the origin of grid cells through the lens of pattern formation
Authors: Ben Sorscher, Gabriel Mel, Surya Ganguli, Samuel Ocko
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Grid cells in the brain fire in strikingly regular hexagonal patterns across space. There are currently two seemingly unrelated frameworks for understanding these patterns. Mechanistic models account for hexagonal firing fields as the result of pattern-forming dynamics in a recurrent neural network with hand-tuned center-surround connectivity. Normative models specify a neural architecture, a learning rule, and a navigational task, and observe that grid-like firing fields emerge due to the constraints of solving this task. Here we provide an analytic theory that unifies the two perspectives by casting the learning dynamics of neural networks trained on navigational tasks as a pattern forming dynamical system. This theory provides insight into the optimal solutions of diverse formulations of the normative task, and shows that symmetries in the representation of space correctly predict the structure of learned firing fields in trained neural networks. Further, our theory proves that a nonnegativity constraint on firing rates induces a symmetry-breaking mechanism which favors hexagonal firing fields.  We extend this theory to the case of learning multiple grid maps and demonstrate that optimal solutions consist of a hierarchy of maps with increasing length scales. These results unify previous accounts of grid cell firing and provide a novel framework for predicting the learned representations of recurrent neural networks.
Functional Adversarial Attacks
Authors: Cassidy Laidlaw, Soheil Feizi
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose functional adversarial attacks, a novel class of threat models for crafting adversarial examples to fool machine learning models. Unlike a standard lp-ball threat model, a functional adversarial threat model allows only a single function to be used to perturb input features to produce an adversarial example. For example, a functional adversarial attack applied on colors of an image can change all red pixels simultaneously to light red. Such global uniform changes in images can be less perceptible than perturbing pixels of the image individually. For simplicity, we refer to functional adversarial attacks on image colors as ReColorAdv, which is the main focus of our experiments. We show that functional threat models can be combined with existing additive (lp) threat models to generate stronger threat models that allow both small, individual perturbations and large, uniform changes to an input. Moreover, we prove that such combinations encompass perturbations that would not be allowed in either constituent threat model. In practice, ReColorAdv can significantly reduce the accuracy of a ResNet-32 trained on CIFAR-10. Furthermore, to the best of our knowledge, combining ReColorAdv with other attacks leads to the strongest existing attack even after adversarial training.
Memory-oriented Decoder for Light Field Salient Object Detection
Authors: Miao Zhang, Jingjing Li, JI WEI, Yongri Piao, Huchuan Lu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Light field data have been demonstrated in favor of many tasks in computer vision, but existing works about light field saliency detection still rely on hand-crafted features. In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection. Our goal is to deeply explore and comprehensively exploit internal correlation of focal slices for accurate prediction by designing feature fusion and integration mechanisms. The success of our method is demonstrated by achieving the state of the art on three datasets. We present this problem in a way that is accessible to members of the community and provide a large-scale light field dataset that facilitates comparisons across algorithms. The code and dataset will be made publicly available.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Authors: Valerio Perrone, Huibin Shen, Matthias W Seeger, Cedric Archambeau, Rodolphe Jenatton
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box functions. In this work, we introduce a method to automatically design the BO search space by relying on evaluations of previous black-box functions. We depart from the common practice of defining a set of arbitrary search ranges a priori by considering search space geometries that are learnt from historical data. This simple, yet effective strategy can be used to endow many existing BO methods with transfer learning properties. Despite its simplicity, we show that our approach considerably boosts BO by reducing the size of the search space, thus accelerating the optimization of a variety of black-box optimization problems. In particular, the proposed approach combined with random search results in a parameter-free, easy-to-implement, robust hyperparameter optimization strategy. We hope it will constitute a natural baseline for further research attempting to warm-start BO.
Image Synthesis with a Single (Robust) Classifier
Authors: Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context.
Learning from Trajectories via Subgoal Discovery
Authors: Sujoy Paul, Jeroen Vanbaar, Amit Roy-Chowdhury
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories could help to learn faster. However, Imitation Learning (IL) via supervised pre-training with these trajectories may not perform as well and generally requires additional finetuning with expert-in-the-loop. In this paper, we propose an approach which uses the expert trajectories and learns to decompose the complex main task into smaller sub-goals. We learn a function which partitions the state-space into sub-goals, which can then be used to design an extrinsic reward function. We follow a strategy where the agent first learns from the trajectories using IL and then switches to Reinforcement Learning (RL) using the identified sub-goals, to alleviate the errors in the IL step. To deal with states which are under-represented by the trajectory set, we also learn a function to modulate the sub-goal predictions. We show that our method is able to solve complex goal-oriented tasks, which other RL, IL or their combinations in literature are not able to solve.
Unsupervised State Representation Learning in Atari
Authors: Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: State representation learning, or the ability to capture latent generative factors of an environment is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations in an unsupervised manner without supervision from rewards is an open problem. We introduce a method that tries to learn better state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Authors: Gregory Farquhar, Shimon Whiteson, Jakob Foerster
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives. We derive an objective that, under automatic differentiation, produces low-variance unbiased estimators of derivatives at any order. Our objective is compatible with arbitrary advantage estimators, which allows the control of the bias and variance of any-order derivatives when using function approximation. Furthermore, we propose a method to trade off bias and variance of higher order derivatives by discounting the impact of more distant causal dependencies. We demonstrate the correctness and utility of our estimator in analytically tractable MDPs and in meta-reinforcement-learning for continuous control.
Meta Learning with Relational Information for Short Sequences
Authors: Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta Learning method for Short Sequences) for learning heterogeneous point process models from a collection of short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptively learning for each individual sequence. We further propose an efficient stochastic variational meta-EM algorithm, which can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events.
Private Learning Implies Online Learning: An Efficient Reduction
Authors: Alon Gonen, Elad Hazan, Shay Moran
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study the relationship between the notions of differentially private learning and online learning. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}. 
Specifically, does an efficient differentially private learner imply an efficient online learner? 
In this paper we resolve this open question in the context of pure differential privacy.
We derive an efficient black-box reduction from differentially private learning to online learning from expert advice.
Learning from brains how to regularize machines
Authors: Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian Sinz, Zachary Pitkow, Andreas Tolias
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g. adversarial noise leading to erroneous decisions. We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity. We presented natural images to mice and measured the responses of thousands of neurons from cortical visual areas. Next, we denoised the notoriously variable neural activity using strong predictive models trained on this large corpus of responses from the mouse visual system, and calculated the representational similarity for millions of pairs of images from the model's predictions. We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones. This preserved performance of baseline models when classifying images under standard benchmarks, while maintaining substantially higher performance compared to baseline or control models when classifying noisy images. Moreover, the models regularized with cortical representations also improved model robustness in terms of adversarial attacks. This demonstrates that regularizing with neural data can be an effective tool to create an inductive bias towards more robust inference.
Kernel quadrature with DPPs
Authors: Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We study quadrature rules for functions living in an RKHS, using nodes sampled from a projection determinantal point process (DPP). DPPs are parametrized by a kernel, and we use a truncated and saturated version of the RKHS kernel.
This natural link between the two kernels, along with DPP machinery, leads to relatively tight bounds on the quadrature error, that depends on the spectrum of the RKHS kernel. Finally, we experimentally compare DPPs to existing kernel-based quadratures such as herding, Bayesian quadrature, or continuous leverage score sampling. Numerical results confirm the interest of DPPs, and even suggest faster rates than our bounds in particular cases.
A Debiased MDI Feature Importance Measure for Random Forests
Authors: Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembles. It has long been known that Mean Decrease Impurity (MDI), one of the most widely used measures of feature importance, incorrectly assigns high importance to noisy features, leading to systematic bias in feature selection. In this paper, we address the feature selection bias of MDI from both theoretical and methodological perspectives. Based on the original definition of MDI by Breiman et al.  \cite{Breiman1984} for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones. However, it is not clear how to reduce the bias of MDI using its existing analytical expression. We derive a new analytical expression for MDI, and based on this new expression, we are able to propose a debiased MDI feature importance measure using out-of-bag samples, called MDI-oob. For both the simulated data and a genomic ChIP dataset, MDI-oob achieves state-of-the-art performance in feature selection from Random Forests for both deep and shallow trees.
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Authors: David Clark, Jesse Livezey, Kristofer Bouchard
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data. However, popular methods disregard temporal structure, rendering them prone to extracting noise rather than meaningful dynamics when applied to time series data. At the same time, many successful unsupervised learning methods for temporal, sequential and spatial data extract features which are predictive of their surrounding context. Combining these approaches, we introduce Dynamical Components Analysis (DCA), a linear dimensionality reduction method which discovers a subspace of high-dimensional time series data with maximal predictive information, defined as the mutual information between the past and future. We test DCA on synthetic examples and demonstrate its superior ability to extract dynamical structure compared to commonly used linear methods. We also apply DCA to several real-world datasets, showing that the dimensions extracted by DCA are more useful than those extracted by other methods for predicting future states and decoding auxiliary variables. Overall, DCA robustly extracts dynamical structure in noisy, high-dimensional data while retaining the computational efficiency and geometric interpretability of linear dimensionality reduction methods.
MintNet: Building Invertible Neural Networks with Masked Convolutions
Authors: Yang Song, Chenlin Meng, Stefano Ermon
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable and very fast in practice. Additionally, the determinant of the Jacobian can be computed analytically and efficiently, enabling their generative use as flow models. To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification. When trained as generative models, our invertible networks achieve competitive likelihoods on MNIST, CIFAR-10 and ImageNet 32x32, with bits per dimension of 0.98, 3.32 and 4.06 respectively.
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Authors: Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani
Published in: Advances in Neural Information Processing Systems 32  (NeurIPS 2019) (Main Conference Track), 2019

Abstract: Modern approaches for multi-person pose estimation in video require large amounts of dense annotations. However, labeling every frame in a video is costly and labor intensive. To reduce the need for dense annotations, we propose a PoseWarper network that leverages training videos with sparse annotations (every k frames) to learn to perform dense temporal pose propagation and estimation. Given a pair of video frames---a labeled Frame A and an unlabeled Frame B---we train our model to predict human pose in Frame A using the features from Frame B by means of deformable convolutions to implicitly learn the pose warping between A and B. We demonstrate that we can leverage our trained PoseWarper for several applications. First, at inference time we can reverse the application direction of our network in order to propagate pose information from manually annotated frames to unlabeled frames. This makes it possible to generate pose annotations for the entire video given only a few manually-labeled frames. Compared to modern label propagation methods based on optical flow, our warping mechanism is much more compact (6M vs 39M parameters), and also more accurate (88.7% mAP vs 83.8% mAP). We also show that we can improve the accuracy of a pose estimator by training it on an augmented dataset obtained by adding our propagated poses to the original manual labels. Lastly, we can use our PoseWarper to aggregate temporal pose information from neighboring frames during inference. This allows us to obtain state-of-the-art pose detection results on PoseTrack2017 and PoseTrack2018 datasets.