nils-herrmann/clean_scirepeval huggingface dataset:
Low-rank Matrix Recovery with Unknown Correspondence
Authors: Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha

We study a matrix recovery problem with unknown correspondence: given the observation matrix $M_o=[A,\tilde P B]$, where $\tilde P$ is an unknown permutation matrix, we aim to recover the underlying matrix $M=[A,B]$. Such problem commonly arises in many applications where heterogeneous data are utilized and the correspondence among them are unknown, e.g., due to privacy concerns. We show that it is possible to recover $M$ via solving a nuclear norm minimization problem under a proper low-rank condition on $M$, with provable non-asymptotic error bound for the recovery of $M$. We propose an algorithm, $\text{M}^3\text{O}$ (Matrix recovery via Min-Max Optimization) which recasts this combinatorial problem as a continuous minimax optimization problem and solves it by proximal gradient with a Max-Oracle. $\text{M}^3\text{O}$ can also be applied to a more general scenario where we have missing entries in $M_o$ and multiple groups of data with distinct unknown correspondence. Experiments on  simulated data, the MovieLens 100K dataset and Yale B database show that $\text{M}^3\text{O}$ achieves state-of-the-art performance over several baselines and can recover the ground-truth correspondence with high accuracy.
The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks
Authors: Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

Miscalibration -- a mismatch between a model's confidence and its correctness -- of Deep Neural Networks (DNNs) makes their predictions hard for downstream components to trust. Ideally, we want networks to be accurate, calibrated and confident. Temperature scaling, the most popular calibration approach, will calibrate a DNN without affecting its accuracy, but it will also make its correct predictions under-confident. In this paper, we show that replacing the widely used cross-entropy loss with focal loss allows us to learn models that are already very well calibrated. When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks. We provide a thorough analysis of the factors causing miscalibration, and use the insights we glean from this to theoretically justify the empirically excellent performance of focal loss. We perform extensive experiments on a variety of computer vision (CIFAR-10/100) and NLP (SST, 20 Newsgroup) datasets, and with a wide variety of different network architectures, and show that our approach achieves state-of-the-art accuracy and calibration in almost all cases.
Neural Topic Model via Optimal Transport
Authors: He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new neural topic model via the theory of optimal transport (OT). Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss. Extensive experiments show that our framework significantly outperforms the state-of-the-art NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts.
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Authors: Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau

Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for model-based policy evaluation and search. Instead of de novo synthesis of data, here we assume logged, real experience and model alternative outcomes of this experience under counterfactual actions, i.e. actions that were not actually taken. Based on this, we propose the Counterfactually-Guided Policy Search (CF-GPS) algorithm for learning policies in POMDPs from off-policy experience. It leverages structural causal models for counterfactual evaluation of arbitrary policies on individual off-policy episodes. CF-GPS can improve on vanilla model-based RL algorithms by making use of available logged data to de-bias model predictions. In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data. We find empirically that these advantages translate into improved policy evaluation and search results on a non-trivial grid-world task. Finally, we show that CF-GPS generalizes the previously proposed Guided Policy Search and that reparameterization-based algorithms such Stochastic Value Gradient can be interpreted as counterfactual methods.
RaPP: Novelty Detection with Reconstruction along Projection Pathway
Authors: Ki Hyun Kim, Sangwoo Shim, Yongsub Lim, Jongseob Jeon, Jeongwoo Choi, Byungchan Kim, Andre S. Yoon

We propose RaPP, a new methodology for novelty detection by utilizing hidden space activation values obtained from a deep autoencoder.
Precisely, RaPP compares input and its autoencoder reconstruction not only in the input space but also in the hidden spaces.
We show that if we feed a reconstructed input to the same autoencoder again, its activated values in a hidden space are equivalent to the corresponding reconstruction in that hidden space given the original input.
In order to aggregate the hidden space activation values, we propose two metrics, which enhance the novelty detection performance.
Through extensive experiments using diverse datasets, we validate that RaPP improves novelty detection performances of autoencoder-based approaches.
Besides, we show that RaPP outperforms recent novelty detection methods evaluated on popular benchmarks.
Robust training with ensemble consensus
Authors: Jisoo Lee, Sae-Young Chung

Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such a memorization issue in the presence of label noise. From the fact that deep neural networks cannot generalize to neighborhoods of memorized features, we hypothesize that noisy examples do not consistently incur small losses on the network under a certain perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) that prevents overfitting to noisy examples by removing them based on the consensus of an ensemble of perturbed networks. One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on noisy MNIST, CIFAR-10, and CIFAR-100 in an efficient manner.
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents
Authors: Enrico Guiraud, Jakob Drefs, Filippos S Panagiotou, Jorg Lucke

Many types of data are generated at least partly by discrete causes that are sparsely
active. To model such data, we here investigate a deep generative model in the
form of a variational autoencoder (VAE) which can learn a sparse, binary code
for its latents. Because of the latents’ discrete nature, standard VAE training is
not possible. The goal of previous approaches has therefore been to amend (i.e.,
typically anneal) discrete priors in order to train discrete VAEs analogously to
conventional ones. Here, we divert much more strongly from conventional VAE
training: We ask if it is also possible to keep the discrete nature of the latents
fully intact by applying a direct, discrete optimization for the encoding model. In
doing so, we (1) sidestep standard VAE mechanisms such as sampling approximation, reparameterization trick and amortization, and (2) observe a much sparser
encoding compared to autoencoders that use annealed discrete latents. Direct optimization of VAEs is enabled by an evolutionary algorithm in conjunction with
truncated posteriors as variational distributions, i.e. by a combination of methods
which is here for the first time applied to a deep model. We first show how the discrete variational method (A) ties into gradient ascent for network weights, and how
it (B) uses the decoder network to select binary latent states for training. Sparse
codes have prominently been applied to image patches, where latents encode edge-like structure. For our VAEs, we maintain this prototypical application domain
and observe the emergence of much sparser codes compared to more conventional
VAEs. To allow for a broad comparison to other approaches, the emerging encoding was then evaluated on denoising and inpainting tasks, which are canonically
benchmarks for image patch models. For datasets with many, large images of single objects (ImageNet, CIFAR etc) deep generative models with dense codes seem
preferable. For image patches, however, we observed advantages of sparse codes
that give rise to state-of-the-art performance in ‘zero-shot’ denoising and inpainting benchmarks. Sparse codes can consequently make VAEs competitive on tasks
where they have previously been outperformed by non-generative approaches.
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
Authors: Victor Quintas-Martinez, Victor Chernozhukov, Vasilis Syrgkanis, Whitney Newey

Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, that is derived based on a functional-specific theoretical derivation of what is known as the influence function and which leads to properties such as double robustness and Neyman orthogonality. We instead implement an automatic debiasing procedure based on automatically learning the Riesz representation of the linear functional using Neural Nets and Random Forests. Our method solely requires value query oracle access to the linear functional. We propose a multi-tasking Neural Net debiasing method with stochastic gradient descent minimization of a combined Reisz representer and regression loss, while sharing representation layers for the two functions. We also propose a random forest method which learns a locally linear representation of the Reisz function. Even though our methodology applies to arbitrary functionals, we experimentally find that it beats state of the art performance of the prior neural net based estimator of Shi et al. (2019) for the case of the average treatment effect functional. We also evaluate our method on the more challenging problem of estimating average marginal effects with continuous treatments, using semi-synthetic data of gasoline price changes on gasoline demand.
Data-Efficient Augmentation for Training Neural Networks
Authors: Tian Yu Liu, Baharan Mirzasoleiman

Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, modern data augmentation techniques become computationally prohibitive for large datasets. To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation. We first show that data augmentation, modeled as additive perturbations, speeds up learning by enlarging the smaller singular values of the network Jacobian. Then, we propose a framework to iteratively extract small subsets of training data that when augmented, closely capture the alignment of the fully augmented Jacobian with label/residual vector. We prove that stochastic gradient descent applied to augmented subsets found by our approach have similar training dynamics to that of fully augmented data. Our experiments demonstrate that our method outperforms state-of-the-art max-loss strategy by 7.7% on CIFAR10 while achieving 6.3x speedup, and by 4.7% on SVHN while achieving 2.2x speedup, using 10% and 30% subsets, respectively.
The (Un)reliability of saliency methods
Authors: Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Kristof T. Schütt, Maximilian Alber, Sven Dähne, Dumitru Erhan, Been Kim

Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step ---adding a mean shift to the input data--- to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute. We define input invariance as the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input. We show, through several examples, that saliency methods that do not satisfy a input invariance property are unreliable and can lead to misleading and inaccurate attribution.
Generalizing Graph Convolutional Networks via Heat Kernel
Authors: Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang

Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs. A recent study shows that GCNs can be simplified as a linear model by removing nonlinearities and weight matrices across all consecutive layers, resulting the simple graph convolution (SGC) model. In this paper, we aim to understand GCNs and generalize SGC as a linear model via heat kernel (HKGCN), which acts as a low-pass filter on graphs and enables the aggregation of information from extremely large receptive fields. We theoretically show that HKGCN is in nature a continuous propagation model and GCNs without nonlinearities (i.e., SGC) are the discrete versions of it. Its low-pass filter and continuity properties facilitate the fast and smooth convergence of feature propagation. Experiments on million-scale networks show that the linear HKGCN model not only achieves consistently better results than SGC but also can match or even beat advanced GCN models, while maintaining SGC’s superiority in efficiency.
Decoupled Weight Decay Regularization
Authors: Ilya Loshchilov, Frank Hutter

L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it ``weight decay'' in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at \url{https://github.com/loshchil/AdamW-and-SGDW}
Contrastive Learning with Stronger Augmentations
Authors: Xiao Wang, Guo-Jun Qi

Representation learning has been greatly improved with the advance of contrastive learning methods with the performance being closer to their supervised learning counterparts. Those methods have greatly benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. Although stronger augmentations could expose novel patterns of representations to improve their generalizability, directly using stronger augmentations in instance discrimination-based contrastive learning may even deteriorate the performance, because the distortions induced from the stronger augmentations could ridiculously change the image structures and thus the transformed images cannot be viewed as the same as the original ones any more. Additional efforts are needed for us to explore the role of the stronger augmentations in further pushing the performance of unsupervised learning to the fully supervised upper bound. Instead of applying the stronger augmentations directly to minimize the contrastive loss, we propose to minimize the distribution divergence between the weakly and strongly augmented images over the representation bank to supervise the retrieval of strongly augmented queries from a pool of candidates. This avoids an overoptimistic assumption that could overfit the strongly augmented queries containing distorted visual structures into the positive targets in the representation bank, while still being able to distinguish them from the negative samples by leveraging the distributions of weakly augmented counterparts. The proposed method achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned. This is almost the same as 76.5% of top-1 accuracy with a fully supervised ResNet-50. Moreover, it outperforms the previous self-supervised and supervised methods on both the transfer learning and object detection tasks.
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
Authors: Jiahui Yu, Thomas Huang

We study how to set the number of channels in a neural network to achieve better accuracy under constrained resources (e.g., FLOPs, latency, memory footprint or model size). A simple and one-shot approach, named AutoSlim, is presented. Instead of training many network samples and searching with reinforcement learning, we train a single slimmable network to approximate the network accuracy of different channel configurations. We then iteratively evaluate the trained slimmable model and greedily slim the layer with minimal accuracy drop. By this single pass, we can obtain the optimized channel configurations under different resource constraints. We present experiments with MobileNet v1, MobileNet v2, ResNet-50 and RL-searched MNasNet on ImageNet classification. We show significant improvements over their default channel configurations. We also achieve better accuracy than recent channel pruning methods and neural architecture search methods with 100X lower search cost.

Notably, by setting optimized channel numbers, our AutoSlim-MobileNet-v2 at 305M FLOPs achieves 74.2% top-1 accuracy, 2.4% better than default MobileNet-v2 (301M FLOPs), and even 0.2% better than RL-searched MNasNet (317M FLOPs). Our AutoSlim-ResNet-50 at 570M FLOPs, without depthwise convolutions, achieves 1.3% better accuracy than MobileNet-v1 (569M FLOPs).
Time2Vec: Learning a Vector Representation of Time
Authors: Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, Marcus Brubaker

Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
Authors: Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang, Hui Lu, Zhihong Tian

State-of-the-art imitation learning (IL) approaches, e.g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse. In this work, we propose SLIL – Stabilized Likelihood-based Imitation Learning – a novel IL approach that directly maximizes the likelihood of observing the expert demonstrations. SLIL is a two-stage optimization framework, where in stage one the expert state distribution is estimated via a new method for denoising continuous normalizing flow, and in stage two the learner policy is trained to match both the expert’s policy and state distribution. Experimental evaluation of SLIL compared with several baselines in ten different physics-based control tasks reveals superior results in terms of learner policy performance, training stability, and mode distribution preservation.
Policy improvement by planning with Gumbel
Authors: Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver

AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search. However, AlphaZero can fail to improve its policy network, if not visiting all actions at the root of a search tree. To address this issue, we propose a policy improvement algorithm based on sampling actions without replacement. Furthermore, we use the idea of policy improvement to replace the more heuristic mechanisms by which AlphaZero selects and uses actions, both at root nodes and at non-root nodes. Our new algorithms, Gumbel AlphaZero and Gumbel MuZero, respectively without and with model-learning, match the state of the art on Go, chess, and Atari, and significantly improve prior performance when planning with few simulations.
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget
Authors: Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance. Readily-available unlabeled data are used to evaluate selection mechanisms, but are not used for model training in conventional pool-based AL. To minimize the labeling budget, we unify unlabeled sample selection and model training based on two principles. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data that improves representation learning and sample selection. Second, we propose a simple yet effective selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. Our experimental results demonstrate superior performance with our proposed principles for limited labeled data compared to alternative AL and SSL combinations.  In addition, we study the AL phenomena of `cold start', which is becoming an increasingly more important factor to enable optimal unification of data labeling, model training and labeling budget minimization.  We propose a measure that is found to be empirically correlated with the AL target loss. This measure can be used to assist in determining the proper start size.