nils-herrmann/clean_scirepeval huggingface dataset:
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Authors: Jiaqi Mu, Pramod Viswanath

Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a {\em very simple}, and yet counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations {\em even stronger}. The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and text classification) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
One Size Doesn't Fit All: Adaptive Label Smoothing
Authors: Ujwal Krothapalli, Lynn Abbott

This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs). CNNs have proven to be very good classifiers and generally localize objects well; however, the loss functions typically used to train classification CNNs do not penalize inability to localize an object, nor do they take into account an object's relative size in the given image. During training on ImageNet-1K almost all approaches use random crops on the images and this transformation sometimes provides the CNN with background only samples. This causes the classifiers to depend on context. Context dependence is harmful for safety-critical applications. We present a novel approach to classification that combines the ideas of objectness and label smoothing during training. Unlike previous methods, we compute a smoothing factor that is \emph{adaptive} based on relative object size within an image. This causes our approach to produce confidences that are grounded in the size of the object being classified instead of relying on context to make the correct predictions. We present extensive results using ImageNet to demonstrate that CNNs trained using adaptive label smoothing are much less likely to be overconfident in their predictions. We show qualitative results using class activation maps and quantitative results using classification and transfer learning tasks. Our approach is able to produce an order of magnitude reduction in confidence when predicting on context only images when compared to baselines. Using transfer learning, we gain $0.021$AP on MS COCO compared to the hard label approach.
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Authors: Juncheng Dong, Simiao Ren, Yang Deng, Omar Khatib, Jordan Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh

Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In many cases, only the magnitude of these functions are observed on various points on the purely imaginary $j\omega$-axis since coherent measurement of their phases is often expensive.  However, it is desirable to retrieve the lost phases from the magnitudes when possible. To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval. Inspired by the Helson and Sarason Theorem,  we recover coefficients of a rational function of Blaschke products using a Blaschke Product Neural Network (BPNN), based upon the magnitude observations as input. The resulting rational function is then used for phase retrieval. We compare the BPNN to conventional deep neural networks (NNs) on several phase retrieval problems, comprising both synthetic and contemporary real-world problems (e.g., metamaterials for which data collection requires substantial expertise and is time consuming). On each phase retrieval problem, we compare against a population of conventional NNs of varying size and hyperparameter settings. Even without any hyper-parameter search, we find that BPNNs consistently outperform the population of optimized NNs in scarce data scenarios, and do so despite being much smaller models. The results can in turn be applied to calculate the refractive index of metamaterials, which is an important problem in emerging areas of material science.
CNNs as Inverse Problem Solvers and Double Network Superresolution
Authors: Cem TARHAN, Gözde BOZDAĞI AKAR

In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. We show how a single neuron is able to provide the optimum solution for inverse problem, given a low resolution image dictionary as an operator. Introducing a new concept called Representation Dictionary Duality, we show that CNN elements (filters) are trained to be representation vectors and then, during reconstruction, used as dictionaries. In the light of theoretical work, we propose a new algorithm which uses two networks with different structures that are separately trained with low and high coherency image patches and show that it performs faster compared to the state-of-the-art algorithms while not sacrificing from performance.
Multi-Mode Deep Matrix and Tensor Factorization
Authors: Jicong Fan

Recently, deep linear and nonlinear matrix factorizations gain increasing attention in the area of machine learning. Existing deep nonlinear matrix factorization methods can only exploit partial nonlinearity of the data and are not effective in handling matrices of which the number of rows is comparable to the number of columns. On the other hand, there is still a gap between deep learning and tensor decomposition. This paper presents a framework of multi-mode deep matrix and tensor factorizations to explore and exploit the full nonlinearity of the data in matrices and tensors. We use the factorization methods to solve matrix and tensor completion problems and prove that our methods have tighter generalization error bounds than conventional matrix and tensor factorization methods. The experiments on synthetic data and real datasets showed that the proposed methods have much higher recovery accuracy than many baselines.
Efficient Empowerment Estimation for Unsupervised Stabilization
Authors: Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin

Intrinsically motivated artificial agents learn advantageous behavior without externally-provided rewards. Previously, it was shown that maximizing mutual information between agent actuators and future states, known as the empowerment principle, enables unsupervised stabilization of dynamical systems at upright positions, which is a prototypical intrinsically motivated behavior for upright standing and walking. This follows from the coincidence between the objective of stabilization and the objective of empowerment. Unfortunately, sample-based estimation of this kind of mutual information is challenging. Recently, various variational lower bounds (VLBs) on empowerment have been proposed as solutions; however, they are often biased, unstable in training, and have high sample complexity. In this work, we propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel, which allows us to efficiently calculate an unbiased estimator of empowerment by convex optimization. We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches. Specifically, we show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images. Consequently, our method opens a path to wider and easier adoption of empowerment for various applications.
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings
Authors: Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick

While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences, it is still an open question if these MLMs specify a principled probability distribution over the space of possible sequences. In this paper, we interpret MLMs as energy-based sequence models and propose two energy parametrizations derivable from the trained MLMs. In order to draw samples correctly from these models, we develop a tractable sampling scheme based on the Metropolis--Hastings Monte Carlo algorithm. In our approach, samples are proposed from the same masked conditionals used for training the masked language models, and they are accepted or rejected based on their energy values according to the target distribution. We validate the effectiveness of the proposed parametrizations by exploring the quality of samples drawn from these energy-based models for both open-ended unconditional generation and a conditional generation task of machine translation. We theoretically and empirically justify our sampling algorithm by showing that the masked conditionals on their own do not yield a Markov chain whose stationary distribution is that of our target distribution, and our approach generates higher quality samples than other recently proposed undirected generation approaches (Wang et al., 2019, Ghazvininejad et al., 2019).
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
Authors: Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger Baker Grosse

In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark designed to test agents’ generalization ability. INT is based on a theorem generator, which provides theoretically infinite data and allows us to measure 6 different types of generalization, each reflecting a distinct challenge, characteristic of automated theorem proving. In addition, provides a fast theorem proving environment with sequence-based and graph-based interfaces, conducive to performing learning-based research. We introduce base-lines with architectures including transformers and graph neural networks (GNNs)for INT. Using INT, we find that transformer-based agents achieve stronger test performance for most of the generalization tasks, despite having much larger out-of-distribution generalization gaps than GNNs. We further find that the addition of Monte Carlo Tree Search (MCTS) at test time helps to prove new theorems.
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Authors: Ashvin Nair, Murtaza Dalal, Abhishek Gupta, Sergey Levine

Reinforcement learning provides an appealing formalism for learning control policies from experience. However, the classic active formulation of reinforcement learning necessitates a lengthy active exploration process for each behavior, making it difficult to apply in real-world settings. If we can instead allow reinforcement learning to effectively use previously collected data to aid the online learning process, where the data could be expert demonstrations or more generally any prior experience, we could make reinforcement learning a substantially more practical tool. While a number of recent methods have sought to learn offline from previously collected data, it remains exceptionally difficult to train a policy with offline data and improve it further with online reinforcement learning. In this paper we systematically analyze why this problem is so challenging, and propose an algorithm that combines sample-efficient dynamic programming with maximum likelihood policy updates, providing a simple and effective framework that is able to leverage large amounts of offline data and then quickly perform online fine-tuning of reinforcement learning policies. We show that our method enables rapid learning of skills with a combination of prior demonstration data and online experience across a suite of difficult dexterous manipulation and benchmark tasks.
ZerO Initialization: Initializing Residual Networks with only Zeros and Ones
Authors: Jiawei Zhao, Florian Tobias Schaefer, Anima Anandkumar

Deep neural networks are usually initialized with random weights, with adequately selected initial variance to ensure stable signal propagation during training. However, there is no consensus on how to select the variance, and this becomes challenging especially as the number of layers grows. In this work, we replace the widely used random weight initialization with a fully deterministic initialization scheme ZerO, which initializes residual networks with only zeros and ones. By augmenting the standard ResNet architectures with a few extra skip connections and Hadamard transforms, ZerO allows us to start the training from zeros and ones entirely. This has many benefits such as improving reproducibility (by reducing the variance over different experimental runs) and allowing network training without batch normalization. Surprisingly, we find that ZerO achieves state-of-the-art performance over various image classification datasets, including ImageNet, which suggests random weights may be unnecessary for modern network initialization.
Distribution-Driven Disjoint Prediction Intervals for Deep Learning
Authors: Jaehak Cho, Jae Myung Kim, Sungyeob Han, Jungwoo Lee

This paper redefines prediction intervals (PIs) as the form of a union of disjoint intervals. PIs represent predictive uncertainty in the regression problem. Since previous PI methods assumed a single continuous PI (one lower and upper bound), it suffers from performance degradation in the uncertainty estimation when the conditional density function has multiple modes. This paper demonstrates that multimodality should be considered in regression uncertainty estimation. To address the issue, we propose a novel method that generates a union of disjoint PIs. Throughout UCI benchmark experiments, our method improves over current state-of-the-art uncertainty quantification methods, reducing an average PI width by over 27$\%$. Through qualitative experiments, we visualized that the multi-mode often exists in real-world datasets and why our method produces high-quality PIs compared to the previous PI.
Learning Graph Normalization for Graph Neural Networks
Authors: Yihao Chen, Xin Tang, Xianbiao Qi, Chun-Guang Li, Rong Xiao

Graph Neural Networks (GNNs) have emerged as a useful paradigm to process graph-structured data. Usually, GNNs are stacked to multiple layers and the node representations in each layer are computed through propagating and aggre- gating the neighboring node features with respect to the graph. To effectively train a GNN with multiple layers, some normalization techniques are necessary. Though the existing normalization techniques have been shown to accelerate train- ing GNNs, the structure information on the graph is ignored yet. In this paper, we propose two graph-aware normalization methods to effectively train GNNs. Then, by taking into account that normalization methods for GNNs are highly task-relevant and it is hard to know in advance which normalization method is the best, we propose to learn attentive graph normalization by optimizing a weighted combination of multiple graph normalization methods at different scales. By op- timizing the combination weights, we can automatically select the best or the best combination of multiple normalization methods for a specific task. We con- duct extensive experiments on benchmark datasets for different tasks and confirm that the graph-aware normalization methods lead to promising results and that the learned weights suggest the more appropriate normalization methods for specific task
On the Certified Robustness for Ensemble Models and Beyond
Authors: Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Bo Li

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs to make arbitrarily incorrect predictions. To defend against such attacks, both empirical and theoretical defense approaches have been proposed for a single ML model. In this work, we aim to explore and characterize the robustness conditions for ensemble ML models. We prove that the diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption. We also show that an ensemble model can achieve higher certified robustness than a single base model based on these conditions. To our best knowledge, this is the first work providing tight conditions for the ensemble robustness. Inspired by our analysis, we propose the lightweight Diversity Regularized Training (DRT) for ensemble models. We derive the certified robustness of DRT based ensembles such as standard Weighted Ensemble and Max-Margin Ensemble following the sufficient and necessary conditions. Besides, to efficiently calculate the model-smoothness, we leverage adapted randomized model smoothing to obtain the certified robustness for different ensembles in practice. We show that the certified robustness of ensembles, on the other hand, verifies the necessity of DRT. To compare different ensembles, we prove that when the adversarial transferability among base models is high, Max-Margin Ensemble can achieve higher certified robustness than Weighted Ensemble; vice versa. Extensive experiments show that ensemble models trained with DRT can achieve the state-of-the-art certified robustness under various settings. Our work will shed light on future analysis for robust ensemble models.
Convergent Adaptive Gradient Methods in Decentralized Optimization
Authors: Xiangyi Chen, Belhal Karimi, Weijie Zhao, Ping Li

Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks, in the past few years. Meanwhile, given the need for distributed training procedures, distributed optimization algorithms are at the center of attention. With the growth of computing power and the need for using machine learning models on mobile devices, the communication cost of distributed training algorithms needs careful consideration. In that regard, more and more attention is shifted from the traditional parameter server training paradigm to the decentralized one, which usually requires lower communication costs. In this paper, we rigorously incorporate adaptive gradient methods into decentralized training procedures and introduce novel convergent decentralized adaptive gradient methods. Specifically, we propose a general algorithmic framework that can convert existing adaptive gradient methods to their decentralized counterparts. In addition, we thoroughly analyze the convergence behavior of the proposed algorithmic framework and show that if a given adaptive gradient method converges, under some specific conditions, then its decentralized counterpart is also convergent.
Uncertainty Sets for Image Classifiers using Conformal Prediction
Authors: Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, Jitendra Malik

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network’s probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.
A Unified framework for randomized smoothing based certified defenses
Authors: Tianhang Zheng, Di Wang, Baochun Li, Jinhui Xu

Randomized smoothing, which was recently proved to be a certified defensive technique, has received considerable attention due to its scalability to large datasets and neural networks. However, several important questions still remain unanswered in the existing frameworks, such as (i) whether Gaussian mechanism is an optimal choice for certifying $\ell_2$-normed robustness, and (ii) whether randomized smoothing can certify $\ell_\infty$-normed robustness (on high-dimensional datasets like ImageNet). To answer these questions, we introduce a {\em  unified} and {\em self-contained} framework to study randomized smoothing-based certified defenses, where we mainly focus on the two most popular norms in adversarial machine learning, {\em i.e.,} $\ell_2$ and $\ell_\infty$ norm. We answer the above two questions by first demonstrating that Gaussian mechanism and  Exponential mechanism are the (near) optimal options to certify the $\ell_2$ and $\ell_\infty$-normed robustness. We further show that the largest $\ell_\infty$ radius certified by randomized smoothing is upper bounded by $O(1/\sqrt{d})$, where $d$ is the dimensionality of the data. This theoretical finding suggests that certifying $\ell_\infty$-normed robustness by randomized smoothing may not be scalable to high-dimensional data. The veracity of our framework and analysis is verified by extensive evaluations on CIFAR10 and ImageNet.
LatentPoison -- Adversarial Attacks On The Latent Space
Authors: Antonia Creswell, Biswa Sengupta, Anil A. Bharath

Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms. Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack. This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.