nils-herrmann/clean_scirepeval huggingface dataset:
Mix-MaxEnt: Creating High Entropy Barriers To Improve Accuracy and Uncertainty Estimates of Deterministic Neural Networks
Authors: Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip Torr, Puneet K. Dokania

We propose an extremely simple approach to regularize a single deterministic neural network to obtain improved accuracy and reliable uncertainty estimates. Our approach, on top of the cross-entropy loss, simply puts an entropy maximization regularizer corresponding to the predictive distribution in the regions of the embedding space between the class clusters. This is achieved by synthetically generating between-cluster samples via the convex combination of two images from different classes and maximizing the entropy on these samples. Such a data-dependent regularization guides the maximum likelihood estimation to prefer a solution that (1) maps out-of-distribution samples to high entropy regions (creating an entropy barrier); and (2) is more robust superficial input perturbations.
Via extensive experiments on real-world datasets (CIFAR-10 and CIFAR-100) using ResNet and Wide-ResNet architectures, we demonstrate that Mix-MaxEnt consistently provides much improved classification accuracy, better calibrated probabilities for in-distribution data, and reliable uncertainty estimates when exposed to situations involving domain-shift and out-of-distribution samples.
Understanding and Mitigating Accuracy Disparity in Regression
Authors: Jianfeng Chi, Han Zhao, Geoff Gordon, Yuan Tian

With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between label populations and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective function. We conduct experiments on four real-world datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models.
GANMEX: Class-Targeted One-vs-One Attributions using GAN-based Model Explainability
Authors: Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin

Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a baseline input for performing feature perturbations, limited research has been conducted to address the baseline selection issues. Poor choices of baselines can lead to unfair attributions as well as limited ability of one-vs-one explanations for multi-class classifiers, which means explaining why the input belongs to its original class but not the other specified target class. Achieving one-vs-one explanation is crucial when certain classes are more similar than others, e.g. two bird types among multiple animals. One-vs-one explanations focus on key differentiating features rather than features shared across the original and the target classes. In this paper, we present GANMEX, a novel algorithm applying Generative Adversarial Networks (GAN) by incorporating the to-be-explained classifier as part of the adversarial networks. Our approach effectively selects the baseline as the closest realistic sample belong to the target class, which allows attribution methods to provide true one-vs-one explanations. We showed that GANMEX baselines improved the saliency maps visually and led to stronger performance on perturbation-based evaluation metrics over the existing baselines. Attribution results with the existing baselines are known to be insensitive to model randomization, and we demonstrated that GANMEX baselines led to better outcome under the randomization sanity checks.
Understanding trained CNNs by indexing neuron selectivity
Authors: Ivet Rafegas, Maria Vanrell, Luís A. Alexandre

The impressive performance and plasticity of convolutional neural networks to solve different vision problems are shadowed by their black-box nature and its consequent lack of full understanding. To reduce this gap we propose to describe the activity of individual neurons by quantifiyng their inherent selectivity to specific properties. Our approach is based on the definition of feature selectivity indexes that allow the ranking of neurons according to specific properties. Here we report the results of exploring selectivity indexes for: (a) an image feature (color); and (b) an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer conv4 or class selective neurons such as dog-face neurons in layer conv5, and establishes a methodology to derive other selectivity properties.  Indexing on neuron selectivity can statistically draw how features and classes are represented through layers at a moment when the size of trained nets is growing and automatic tools to index can be helpful.
Necessary and Sufficient Conditions for Compositional Representations
Authors: Yuanpeng Li

Humans leverage compositionality for flexible and efficient learning, but current machine learning algorithms lack such ability. Despite many efforts in specific cases, there is still absence of theories and tools to study it systematically. In this paper, we leverage group theory to mathematically prove necessary and sufficient conditions for two fundamental questions of compositional representations. (1) What are the properties for a set of components to be expressed compositionally. (2) What are the properties for mappings between compositional and entangled representations. We provide examples to better understand the conditions and how to apply them. E.g., we use the theory to give a new explanation of why attention mechanism helps compositionality. We hope this work will help to advance understanding of compositionality and improvement of artificial intelligence towards human level.
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
Authors: Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan

Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data [Nalisnick et al., 2019; Choi et al., 2019].  We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density.  In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed [Bishop, 1994].  To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods.  The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated.  We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. [2019].
Discrete flow posteriors for variational inference in discrete dynamical systems
Authors: Laurence Aitchison, Vincent Adam, Srinivas C. Turaga

Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism.  However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior.  While it is possible to use normalizing flow approximate posteriors for continuous latents, there is nothing analogous for discrete latents. The most natural approach to model discrete dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow.  We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space models.  To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow.  We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach.  We tested our approach on the neuroscience problem of inferring discrete spiking activity from noisy calcium-imaging data, and found that it gave accurate connectivity estimates in an order of magnitude less time.
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes
Authors: Dani Kiyasseh, Tingting Zhu, David A. Clifton

The ongoing digitization of health records within the healthcare industry results in large-scale datasets. Manually extracting clinically-useful insight from such datasets is non-trivial. However, doing so at scale while simultaneously leveraging patient-specific attributes such as sex and age can assist with clinical-trial enrollment, medical school educational endeavours, and the evaluation of the fairness of neural networks. To facilitate the reliable extraction of clinical information, we propose to learn embeddings, known as clinical prototypes (CPs), via supervised contrastive learning. We show that CPs can be efficiently used for large-scale retrieval and clustering of physiological signals based on multiple patient attributes. We also show that CPs capture attribute-specific semantic relationships.
Value-aware transformers for 1.5d data
Authors: James F Cann, Timothy J Roberts, Amy R Tso, Amy Nelson, Parashkev Nachev

Sparse sequential highly-multivariate data of the form characteristic of hospital in-patient investigation and treatment poses a considerable challenge for representation learning. Such data is neither faithfully reducible to 1d nor dense enough to constitute multivariate series. Conventional models compromise their data by requiring these forms at the point of input. Building on contemporary sequence-modelling architectures we design a value-aware transformer, prompting a reconceptualisation of our data as 1.5-dimensional: a token-value form both respecting its sequential nature and augmenting it with a quantifier. Experiments focused on sequential in-patient laboratory data up to 48hrs after hospital admission show that the value-aware transformer performs favourably versus competitive baselines on in-hospital mortality and length-of-stay prediction within the MIMIC-III dataset.
CorrAttack: Black-box Adversarial Attack with Structured Search
Authors: Zhichao Huang, Yaowei Huang, Tong Zhang

We present a new method for score-based adversarial attack, where the attacker queries the loss-oracle of the target model.
Our method employs a parameterized search space with a structure that captures the relationship of the gradient of the loss function. We show that searching over the structured space can be approximated by a time-varying contextual bandits problem, where the attacker takes feature of the associated arm to make modifications of the input, and receives an immediate reward as the reduction of the loss function. The time-varying contextual bandits problem can then be solved by a Bayesian optimization procedure, which can take advantage of the features of the structured action space. The experiments on ImageNet and the Google Cloud Vision API demonstrate that the proposed method achieves the state of the art success rates and query efficiencies for both undefended and defended models.
DppNet: Approximating Determinantal Point Processes with Deep Networks
Authors: Zelda Mariet, Jasper Snoek, Yaniv Ovadia

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 DPP so powerful and unique.  Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.
Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
Authors: David W Zhang, Gertjan J. Burghouts, Cees G. M. Snoek

This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction for applications in particle physics, biological systems and combinatorial optimization. We address two common scaling problems encountered in set-to-hypergraph tasks that limit the size of the input set: the exponentially growing number of hyperedges and the run-time complexity, both leading to higher memory requirements. We make three contributions. First, we propose to predict and supervise the \emph{positive} edges only, which changes the asymptotic memory scaling from exponential to linear. Second, we introduce a training method that encourages iterative refinement of the predicted hypergraph, which allows us to skip iterations in the backward pass for improved efficiency and constant memory usage. Third, we combine both contributions in a single set-to-hypergraph model that enables us to address problems with larger input set sizes. We provide ablations for our main technical contributions and show that our model outperforms prior state-of-the-art, especially for larger sets.
Information-Theoretic Generalization Bounds for Iterative Semi-Supervised Learning
Authors: Haiyun He, Hanshu YAN, Vincent Tan

We consider iterative semi-supervised learning (SSL) algorithms that iteratively generate pseudo-labels for a large amount unlabelled data  to progressively refine the model parameters. In particular, we seek to understand the behaviour of the {\em generalization error} of iterative SSL algorithms using information-theoretic principles. To obtain bounds that are amenable to numerical evaluation, we first work with a simple model---namely, the binary Gaussian mixture model. Our theoretical results suggest that when the class conditional variances are not too large, the upper bound on the generalization error decreases monotonically with the number of iterations, but quickly saturates. The theoretical results on the simple model are corroborated by extensive experiments on  several benchmark datasets such as the MNIST and CIFAR datasets in which we notice that the generalization error improves after several pseudo-labelling iterations, but saturates afterwards.
ROS-HPL: Robotic Object Search with Hierarchical Policy Learning and Intrinsic-Extrinsic Modeling
Authors: Xin Ye, Shibin Zheng, Yezhou Yang

Despite significant progress in Robotic Object Search (ROS) over the recent years with deep reinforcement learning based approaches, the sparsity issue in reward setting as well as the lack of interpretability of the previous ROS approaches leave much to be desired. We present a novel policy learning approach for ROS, based on a hierarchical and interpretable modeling with intrinsic/extrinsic reward setting, to tackle these two challenges. More specifically, we train the low-level policy by deliberating between an action that achieves an immediate sub-goal and the one that is better suited for achieving the final goal. We also introduce a new evaluation metric, namely the extrinsic reward, as a harmonic measure of the object search success rate and the average steps taken. Experiments conducted with multiple settings on the House3D environment validate and show that the intelligent agent, trained  with our model, can achieve a better object search performance (higher success rate with lower average steps, measured by SPL: Success weighted by inverse Path Length). In addition, we conduct studies w.r.t. the parameter that controls the weighted overall reward from intrinsic and extrinsic components. The results suggest it is critical to devise a proper trade-off strategy to perform the object search well.
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning
Authors: Yutong Dai, Xingjun Ma, Lichao Sun

Federated learning (FL) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data. However, recent studies have shown that FL is vulnerable to weight poisoning attacks. In this paper, we propose a probabilistic discretization mechanism on the client side, which transforms the client's model weight into a vector that can only have two different values but still guarantees that the server obtains an unbiased estimation of the client's model weight.  We theoretically analyze the utility, robustness, and convergence of our proposed discretization mechanism and empirically verify its superior robustness against various weight-based attacks under the cross-device FL setting.
Preliminary theoretical troubleshooting in Variational Autoencoder
Authors: Shiqi Liu, Qian Zhao, Xiangyong Cao, Deyu Meng, Zilu Ma, Tao Yu

What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE? This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.  On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension. Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension. On disentanglement issue,   subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made. On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class.  On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors. On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.
Learning and Planning with a Semantic Model
Authors: Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.
Trojans and Adversarial Examples: A Lethal Combination
Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Hai Phan

In this work, we naturally unify adversarial examples and Trojan backdoors into a new stealthy attack, that is activated only when 1) adversarial perturbation is injected into the input examples and 2) a Trojan backdoor is used to poison the training process simultaneously. Different from traditional attacks, we leverage adversarial noise in the input space to move Trojan-infected examples across the model decision boundary, thus making it difficult to be detected. Our attack can fool the user into accidentally trusting the infected model as a robust classifier against adversarial examples. We perform a thorough analysis and conduct an extensive set of experiments on several benchmark datasets to show that our attack can bypass existing defenses with a success rate close to 100%.
Limitations of Active Learning With Deep Transformer Language Models
Authors: Mike D'Arcy, Doug Downey

Active Learning (AL) has the potential to reduce labeling cost when training natural language processing models, but its effectiveness with the large pretrained transformer language models that power today's NLP is uncertain.  We present experiments showing that when applied to modern pretrained models, active learning offers inconsistent and often poor performance.  As in prior work, we find that AL sometimes selects harmful "unlearnable" collective outliers, but we discover that some failures have a different explanation: the examples AL selects are informative but also increase training instability, reducing average performance.  Our findings suggest that for some datasets this instability can be mitigated by training multiple models and selecting the best on a validation set, which we show impacts relative AL performance comparably to the outlier-pruning technique from prior work while also increasing absolute performance. Our experiments span three pretrained models, ten datasets, and four active learning approaches.
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
Authors: catalin ionescu, tejas kulkarni, aaron van de oord, andriy mnih, vlad mnih

Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy? We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well. We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning. These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards. We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.