pwc-archive/papers-with-abstracts huggingface dataset:
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Authors: Shunyu Yao, Rohan Rao, Matthew Hausknecht, Karthik Narasimhan
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Action Generation, Language Modeling, Language Modelling, text-based games

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at https://github.com/princeton-nlp/calm-textgame.
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Authors: Jaehoon Choi, Dongki Jung, Donghwan Lee, Changick Kim
Date: 2020-10-06 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Multi-Task Learning

Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtruth depth map, the photometric loss enables to provide self-supervision on depth prediction by matching the input image frames. However, the photometric loss causes various problems, resulting in less accurate depth values compared with supervised approaches. In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss. Our key idea is to exploit semantic-aware depth features that integrate the semantic and geometric knowledge. Therefore, we introduce multi-task learning schemes to incorporate semantic-awareness into the representation of depth features. Experiments on KITTI dataset demonstrate that our methods compete or even outperform the state-of-the-art methods. Furthermore, extensive experiments on different datasets show its better generalization ability and robustness to various conditions, such as low-light or adverse weather.
Iterative Methods for Computing Eigenvectors of Nonlinear Operators
Authors: Guy Gilboa
Date: 2020-10-06 00:00:00

In this chapter we are examining several iterative methods for solving nonlinear eigenvalue problems. These arise in variational image-processing, graph partition and classification, nonlinear physics and more. The canonical eigenproblem we solve is $T(u)=\lambda u$, where $T:\R^n\to \R^n$ is some bounded nonlinear operator. Other variations of eigenvalue problems are also discussed. We present a progression of 5 algorithms, coauthored in recent years by the author and colleagues. Each algorithm attempts to solve a unique problem or to improve the theoretical foundations. The algorithms can be understood as nonlinear PDE's which converge to an eigenfunction in the continuous time domain. This allows a unique view and understanding of the discrete iterative process. Finally, it is shown how to evaluate numerically the results, along with some examples and insights related to priors of nonlinear denoisers, both classical algorithms and ones based on deep networks.
Testing Tail Weight of a Distribution Via Hazard Rate
Authors: Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld
Date: 2020-10-06 00:00:00

Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data. We study one such problem in the framework of distribution property testing, characterizing the number of samples required to to distinguish whether a distribution has a certain property or is far from having that property. In particular, given samples from a distribution, we seek to characterize the tail of the distribution, that is, understand how many elements appear infrequently. We develop an algorithm based on a careful bucketing scheme that distinguishes light-tailed distributions from non-light-tailed ones with respect to a definition based on the hazard rate, under natural smoothness and ordering assumptions. We bound the number of samples required for this test to succeed with high probability in terms of the parameters of the problem, showing that it is polynomial in these parameters. Further, we prove a hardness result that implies that this problem cannot be solved without any assumptions.
COD3S: Diverse Generation with Discrete Semantic Signatures
Authors: Nathaniel Weir, João Sedoc, Benjamin Van Durme
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Diversity, Semantic Textual Similarity, Sentence

We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seq models typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply COD3S to causal generation, the task of predicting a proposition's plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.
LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU
Authors: Evgeny Ponomarev, Sergey Matveev, Ivan Oseledets
Date: 2020-10-06 00:00:00
Tasks: CPU, GPU, Neural Architecture Search, Prediction

A lot of deep learning applications are desired to be run on mobile devices. Both accuracy and inference time are meaningful for a lot of them. While the number of FLOPs is usually used as a proxy for neural network latency, it may be not the best choice. In order to obtain a better approximation of latency, research community uses look-up tables of all possible layers for latency calculation for the final prediction of the inference on mobile CPU. It requires only a small number of experiments. Unfortunately, on mobile GPU this method is not applicable in a straight-forward way and shows low precision. In this work, we consider latency approximation on mobile GPU as a data and hardware-specific problem. Our main goal is to construct a convenient latency estimation tool for investigation(LETI) of neural network inference and building robust and accurate latency prediction models for each specific task. To achieve this goal, we build open-source tools which provide a convenient way to conduct massive experiments on different target devices focusing on mobile GPU. After evaluation of the dataset, we learn the regression model on experimental data and use it for future latency prediction and analysis. We experimentally demonstrate the applicability of such an approach on a subset of popular NAS-Benchmark 101 dataset and also evaluate the most popular neural network architectures for two mobile GPUs. As a result, we construct latency prediction model with good precision on the target evaluation subset. We consider LETI as a useful tool for neural architecture search or massive latency evaluation. The project is available at https://github.com/leti-ai
Dif-MAML: Decentralized Multi-Agent Meta-Learning
Authors: Mert Kayaalp, Stefan Vlaski, Ali H. Sayed
Date: 2020-10-06 00:00:00
Tasks: Meta-Learning

The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks. As such, meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited to this decentralized setting, where the learner would be able to benefit from information and computational power spread across the agents. Motivated by this observation, in this work, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
LOGAN: Local Group Bias Detection by Clustering
Authors: Jieyu Zhao, Kai-Wei Chang
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Bias Detection, BIG-bench Machine Learning, Clustering

Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.
Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows
Authors: Jan Ackmann, Peter D. Düben, Tim N. Palmer, Piotr K. Smolarkiewicz
Date: 2020-10-06 00:00:00
Tasks: BIG-bench Machine Learning

It is tested whether machine learning methods can be used for preconditioning to increase the performance of the linear solver -- the backbone of the semi-implicit, grid-point model approach for weather and climate models. Embedding the machine-learning method within the framework of a linear solver circumvents potential robustness issues that machine learning approaches are often criticized for, as the linear solver ensures that a sufficient, pre-set level of accuracy is reached. The approach does not require prior availability of a conventional preconditioner and is highly flexible regarding complexity and machine learning design choices. Several machine learning methods are used to learn the optimal preconditioner for a shallow-water model with semi-implicit timestepping that is conceptually similar to more complex atmosphere models. The machine-learning preconditioner is competitive with a conventional preconditioner and provides good results even if it is used outside of the dynamical range of the training dataset.
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration
Authors: Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, Reut Tsarfaty
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Morphological Disambiguation

One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers. In this paper we address the issue of unbalanced morphological ambiguities in Hebrew. We offer a challenge set for Hebrew homographs -- the first of its kind -- containing substantial attestation of each analysis of 21 Hebrew homographs. We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity. Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95. Our resulting annotated datasets are made publicly available for further research.
Directional Graph Networks
Authors: Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò
Date: 2020-10-06 00:00:00
Tasks: Data Augmentation, Graph Classification, Graph Regression, Node Classification

The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according to topologicaly-derived directional flows. First, by defining a vector field in the graph, we develop a method of applying directional derivatives and smoothing by projecting node-specific messages into the field. Then, we propose the use of the Laplacian eigenvectors as such vector field. We show that the method generalizes CNNs on an $n$-dimensional grid and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test. We evaluate our method on different standard benchmarks and see a relative error reduction of 8% on the CIFAR10 graph dataset and 11% to 32% on the molecular ZINC dataset, and a relative increase in precision of 1.6% on the MolPCBA dataset. An important outcome of this work is that it enables graph networks to embed directions in an unsupervised way, thus allowing a better representation of the anisotropic features in different physical or biological problems.
Learn to Synchronize, Synchronize to Learn
Authors: Pietro Verzelli, Cesare Alippi, Lorenzo Livi
Date: 2020-10-06 00:00:00
Tasks: BIG-bench Machine Learning

In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by Generalized Synchronization (GS) when training a RC to solve a generic task. In particular, we show how GS allows the reservoir to correctly encode the system generating the input signal into its dynamics. We also discuss necessary and sufficient conditions for the learning to be feasible in this approach. Moreover, we explore the role that ergodicity plays in this process, showing how its presence allows the learning outcome to apply to multiple input trajectories. Finally, we show that satisfaction of the GS can be measured by means of the Mutual False Nearest Neighbors index, which makes effective to practitioners theoretical derivations.
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Authors: Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden Lake
Date: 2020-10-06 00:00:00
Tasks: Meta-Learning, Systematic Generalization

Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals ("objects that could fall on one's head"). In contrast, standard classification benchmarks: 1) consider only a fixed set of category labels, 2) do not evaluate compositional concept learning and 3) do not explicitly capture a notion of reasoning under uncertainty. We introduce a new few-shot, meta-learning benchmark, Compositional Reasoning Under Uncertainty (CURI) to bridge this gap. CURI evaluates different aspects of productive and systematic generalization, including abstract understandings of disentangling, productive generalization, learning boolean operations, variable binding, etc. Importantly, it also defines a model-independent "compositionality gap" to evaluate the difficulty of generalizing out-of-distribution along each of these axes. Extensive evaluations across a range of modeling choices spanning different modalities (image, schemas, and sounds), splits, privileged auxiliary concept information, and choices of negatives reveal substantial scope for modeling advances on the proposed task. All code and datasets will be available online.
CoRe: Color Regression for Multicolor Fashion Garments
Authors: Alexandre Rame, Arthur Douillard, Charles Ollion
Date: 2020-10-06 00:00:00
Tasks: regression

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs
Authors: Haiyang Zhang, Alison Sneyd, Mark Stevenson
Date: 2020-10-06 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Word Embeddings, Word Similarity

It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy "man is to computer-programmer as woman is to homemaker" is due to word similarity rather than societal bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
Authors: Xingchen Ma, Matthew B. Blaschko
Date: 2020-10-06 00:00:00
Tasks: Bayesian Optimization, global-optimization, Neural Network Compression, SMAC, SMAC+

Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, on a neural network compression problem, on pruning pre-trained VGG16 and ResNet50 models as well as on searching activation functions of ResNet20. Experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).
Safety Aware Reinforcement Learning (SARL)
Authors: Santiago Miret, Somdeb Majumdar, Carroll Wainwright
Date: 2020-10-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As reinforcement learning agents become increasingly integrated into complex, real-world environments, designing for safety becomes a critical consideration. We specifically focus on researching scenarios where agents can cause undesired side effects while executing a policy on a primary task. Since one can define multiple tasks for a given environment dynamics, there are two important challenges. First, we need to abstract the concept of safety that applies broadly to that environment independent of the specific task being executed. Second, we need a mechanism for the abstracted notion of safety to modulate the actions of agents executing different policies to minimize their side-effects. In this work, we propose Safety Aware Reinforcement Learning (SARL) - a framework where a virtual safe agent modulates the actions of a main reward-based agent to minimize side effects. The safe agent learns a task-independent notion of safety for a given environment. The main agent is then trained with a regularization loss given by the distance between the native action probabilities of the two agents. Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training. We contrast this with solutions that rely on task-specific regularization metrics and test our framework on the SafeLife Suite, based on Conway's Game of Life, comprising a number of complex tasks in dynamic environments. We show that our solution is able to match the performance of solutions that rely on task-specific side-effect penalties on both the primary and safety objectives while additionally providing the benefit of generalizability and portability.