nils-herrmann/clean_scirepeval huggingface dataset:
Deterministic Variational Inference for Robust Bayesian Neural Networks
Authors: Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches.
Overcoming Multi-model Forgetting
Authors: Yassine Benyahia*, Kaicheng Yu*, Kamil Bennani-Smires, Martin Jaggi, Anthony Davison, Mathieu Salzmann, Claudiu Musat

We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.
Learning to Understand Goal Specifications by Modelling Reward
Authors: Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples.  As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed.
In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
Authors: Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel

The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets, termed PASCAL-C, COCO-C and Cityscapes-C, contain a large variety of image corruptions. We show that a range of standard object detection models suffer a severe performance loss on corrupted images (down to 30-60% of the original performance). However, a simple data augmentation trick - stylizing the training images - leads to a substantial increase in robustness across corruption type, severity and dataset. We envision our comprehensive benchmark to track future progress towards building robust object detection models. Benchmark, code and data are available at: (hidden for double blind review)
Support-set bottlenecks for video-text representation learning
Authors: Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander G Hauptmann, Joao F. Henriques, Andrea Vedaldi

The dominant paradigm for learning video-text representations – noise contrastive learning – increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behaviour is too strict, enforcing dissimilar representations even for samples that are semantically-related – for example, visually similar videos or ones that share the same depicted action. In this paper, we propose a novel method that alleviates this by leveraging a generative model to naturally push these related samples together: each sample’s caption must be reconstructed as a weighted combination of a support set of visual representations. This simple idea ensures that representations are not overly-specialized to individual samples, are reusable across the dataset, and results in representations that explicitly encode semantics shared between samples, unlike noise contrastive learning. Our proposed method outperforms others by a large margin on MSR-VTT, VATEX, ActivityNet, and MSVD for video-to-text and text-to-video retrieval.
Optimistic Policy Optimization with General Function Approximations
Authors: Qi Cai, Zhuoran Yang, Csaba Szepesvari, Zhaoran Wang

Although policy optimization with neural networks has a track record of achieving state-of-the-art results in reinforcement learning on various domains, the theoretical understanding of the computational and sample efficiency of policy optimization remains restricted to linear function approximations with finite-dimensional feature representations, which hinders the design of principled, effective, and efficient algorithms. To this end, we propose an optimistic model-based policy optimization algorithm, which allows general function approximations while incorporating~exploration. In the episodic setting, we establish a $\sqrt{T}$-regret that scales polynomially in the eluder dimension of the general model class. Here $T$ is the number of steps taken by the agent. In particular, we specialize such a regret to handle two nonparametric model classes; one based on reproducing kernel Hilbert spaces and another based on overparameterized neural networks.
Exploration in Reinforcement Learning with Deep Covering Options
Authors: Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris

While many option discovery methods have been proposed to accelerate exploration in reinforcement learning, they are often heuristic. Recently, covering options was proposed to discover a set of options that provably reduce the upper bound of the environment's cover time, a measure of the difficulty of exploration. Covering options are computed using the eigenvectors of the graph Laplacian, but they are constrained to tabular tasks and are not applicable to tasks with large or continuous state-spaces. 
We introduce deep covering options, an online method that extends covering options to large state spaces,  automatically discovering task-agnostic options that encourage exploration. We evaluate our method in several challenging sparse-reward domains and we show that our approach identifies less explored regions of the state-space and successfully generates options to visit these regions, substantially improving both the exploration and the total accumulated reward.
Prestopping: How Does Early Stopping Help Generalization Against Label Noise?
Authors: Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee

Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized. Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point. Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use. Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4–8.2 percent points under existence of real-world noise.
Zero-Cost Operation Scoring in Differentiable Architecture Search
Authors: Lichuan Xiang, Łukasz Dudziak, Mohamed S Abdelfattah, Thomas Chun Pong Chau, Nicholas Donald Lane, Hongkai Wen

Differentiable neural architecture search (NAS) has attracted significant attention in recent years due to its ability to quickly discover promising architectures of deep neural networks even in very large search spaces. Despite its success, many differentiable NAS methods lack robustness and may degenerate to trivial architectures with excessive parameter-free operations such as skip connections thus leading to inferior performance. In fact, selecting operations based on the magnitude of architectural parameters was recently proven to be fundamentally wrong, showcasing the need to rethink how operation scoring and selection occurs in differentiable NAS. To this end, we formalize and analyze a fundamental component of differentiable NAS: local "operation scoring" that occurs at each choice of operation.
When comparing existing operation scoring functions, we find that existing methods can be viewed as inexact proxies for accuracy.
We also find that existing methods perform poorly when analyzed empirically on NAS benchmarks. From this perspective, we introduce new training-free proxies to the context of differentiable NAS, and show that we can significantly speed up the search process while improving accuracy on multiple search spaces. We take inspiration from zero-cost proxies that were recently studied in the context of sample-based NAS but shown to degrade significantly for larger search spaces like DARTS. Our novel "perturbation-based zero-cost operation scoring" (Zero-Cost-PT) improves searching time and accuracy compared to the best available differentiable architecture search for many search space sizes, including very large ones. Specifically, we are able improve accuracy compared to the best current method (DARTS-PT) on the DARTS CNN search space while being over 40x faster (total searching time 25 minutes on a single GPU). Our code is available at: https://github.com/avail-upon-acceptance.
WaveFlow: A Compact Flow-based Model for Raw Audio
Authors: Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song

In this work, we present WaveFlow, a small-footprint generative flow for raw audio, which is trained with maximum likelihood without complicated density distillation and auxiliary losses as used in Parallel WaveNet.  It provides a unified view of flow-based models for raw audio, including autoregressive flow (e.g., WaveNet) and bipartite flow (e.g., WaveGlow) as special cases. We systematically study these likelihood-based generative models for raw waveforms in terms of test likelihood and speech fidelity. We demonstrate that WaveFlow can synthesize high-fidelity speech and obtain comparable likelihood as WaveNet, while only requiring a few sequential steps to generate very long waveforms.  In particular, our small-footprint WaveFlow has only 5.91M parameters and can generate 22.05kHz speech 15.39 times faster than real-time on a GPU without customized inference kernels.
Using Ontologies To Improve Performance In Massively Multi-label Prediction
Authors: Ethan Steinberg, Peter J. Liu

Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels.  We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
Authors: Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Michael Psenka, Xiaojun Yuan, Heung-Yeung Shum, Yi Ma

This work proposes a new computational framework for automatically learning a closed-loop transcription between multi-class multi-dimensional data and a linear discriminative representation (LDR) that consists of multiple multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as the equilibrium point of a two-player minimax game between the encoder and decoder. A natural  utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation avoids expensive evaluating and minimizing approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, conceptually and computationally this new formulation unifies the benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for complex multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark datasets demonstrate tremendous potential of this framework: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and often better than existing methods based on GAN, VAE or a combination of both.
One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features
Authors: Stephen Casper, Max Nadeau, Gabriel Kreiman

It is well understood that modern deep networks are vulnerable to adversarial attacks. However, conventional methods fail to produce adversarial perturbations that are intelligible to humans, and they pose limited threats in the physical world. To study feature-class associations in networks and better understand the real-world threats they face, we develop feature-level adversarial perturbations using deep image generators and a novel optimization objective. We term these feature-fool attacks. We show that they are versatile and use them to generate targeted feature-level attacks at the ImageNet scale that are simultaneously interpretable, universal to any source image, and physically-realizable. These attacks can also reveal spurious, semantically-describable feature/class associations, and we use them to guide the design of ``copy/paste'' adversaries in which one natural image is pasted into another to cause a targeted misclassification.
Distributional Reinforcement Learning with Monotonic Splines
Authors: Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart

Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal differences. Existing algorithms use step functions or piecewise linear functions. In this paper, we propose to learn smooth continuous quantile functions represented by monotonic rational-quadratic splines, which also naturally solve the quantile crossing problem. Experiments in stochastic environments show that a dense estimation for quantile functions enhances distributional RL in terms of faster empirical convergence and higher rewards in most cases.
Learning from Demonstration with Weakly Supervised Disentanglement
Authors: Yordan Hristov, Subramanian Ramamoorthy

Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teach- ing robots, is difficult in this setting as there is potential for mismatch between human and machine comprehension of the rich data streams. We treat the task of interpretable learning from demonstration as an optimisation problem over a probabilistic generative model. To account for the high-dimensionality of the data, a high-capacity neural network is chosen to represent the model. The latent variables in this model are explicitly aligned with high-level notions and concepts that are manifested in a set of demonstrations. We show that such alignment is best achieved through the use of labels from the end user, in an appropriately restricted vocabulary, in contrast to the conventional approach of the designer picking a prior over the latent variables. Our approach is evaluated in the context of two table-top robot manipulation tasks performed by a PR2 robot – that of dabbing liquids with a sponge (forcefully pressing a sponge and moving it along a surface) and pouring between different containers. The robot provides visual information, arm joint positions and arm joint efforts. We have made videos of the tasks and data available - see supplementary materials at: https://sites.google.com/view/weak-label-lfd.
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Authors: Xuezhe Ma, Chunting Zhou, Eduard Hovy

Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. However, recent studies demonstrate that, when equipped with expressive generative distributions (aka. decoders), VAE suffers from learning uninformative latent representations with the observation called KL Varnishing, in which case VAE collapses into an unconditional generative model. In this work, we introduce mutual posterior-divergence regularization, a novel regularization that is able to control the geometry of the latent space to accomplish meaningful representation learning, while achieving comparable or superior capability of density estimation.Experiments on three image benchmark datasets demonstrate that, when equipped with powerful decoders, our model performs well both on density estimation and representation learning.
Mirror Descent View For Neural Network Quantization
Authors: Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania

Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. NN quantization is usually formulated as a constrained optimization problem and optimized via a modified version of gradient descent. In this work, by interpreting the continuous parameters (unconstrained) as the dual of the quantized ones, we introduce a Mirror Descent (MD) framework (Bubeck (2015)) for NN quantization. Specifically, we provide conditions on the projections (i.e., mapping from continuous to quantized ones) which would enable us to derive valid mirror maps and in turn the respective MD updates. Furthermore, we discuss a numerically stable implementation of MD by storing an additional set of auxiliary dual variables (continuous). This update is strikingly analogous to the popular Straight Through Estimator (STE) based method which is typically viewed as a “trick” to avoid vanishing gradients issue but here we show that it is an implementation method for MD for certain projections. Our experiments on standard classification datasets (CIFAR-10/100, ) with convolutional and residual architectures show that our MD variants obtain fully-quantized networks with accuracies very close to the floating-point networks.
Watching the World Go By: Representation Learning from Unlabeled Videos
Authors: Daniel Gordon, Kiana Ehsani, Dieter Fox, Ali Farhadi

Recent unsupervised representation learning techniques show remarkable success on many single image tasks by using instance discrimination:  learning to differentiate between two augmented versions of the same image and a large batch of unrelated images.  Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial waysand are not aligned with how objects actually change e.g. occlusion, deformation,viewpoint change.  We argue that videos offer this natural augmentation for free.Videos can provide entirely new views of objects,  show deformation,  and even connect semantically similar but visually distinct concepts.We propose Video Noise Contrastive Estimation,  a method for using unlabeled video to learnstrong, transferable, single image representations.We demonstrate improvements over recent unsupervised single image techniques,as well as over fully supervised ImageNet pretraining, across temporal and non-temporal tasks.
ADA+: A GENERIC FRAMEWORK WITH MORE ADAPTIVE EXPLICIT ADJUSTMENT FOR LEARNING RATE
Authors: Yue Zhao, Xiangsheng Huang, Ludan Kou

Although adaptive algorithms have achieved significant success in training deep neural networks with faster training speed, they tend to have poor generalization performance compared to SGD with Momentum(SGDM). One of the state-of-the-art algorithms, PADAM, is proposed to close the generalization gap of adaptive methods while lacking an internal explanation. This work pro- poses a general framework, in which we use an explicit function Φ(·) as an adjustment to the actual step size, and present a more adaptive specific form AdaPlus(Ada+). Based on this framework, we analyze various behaviors brought by different types of Φ(·), such as a constant function in SGDM, a linear function in Adam, a concave function in Padam and a concave function with offset term in AdaPlus. Empirically, we conduct experiments on classic benchmarks both in CNN and RNN architectures and achieve better performance(even than SGDM).