nils-herrmann/clean_scirepeval huggingface dataset:
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
Authors: Zhiyu Chong, Xinzhu Ma, Hong Zhang, Yuxin Yue, Haojie Li, Zhihui Wang, Wanli Ouyang

3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or LiDAR-based methods. However, accurately locating objects in the 3D space from a single image is extremely difficult due to the lack of spatial cues. To mitigate this issue, we propose a simple and effective scheme to introduce the spatial information from LiDAR signals to the monocular 3D detectors, without introducing any extra cost in the inference phase. In particular, we first project the LiDAR signals into the image plane and align them with the RGB images. After that, we use the resulting data to train a 3D detector (LiDAR Net) using the same architecture as the baseline model. Finally, this LiDAR Net can serve as the teacher to transfer the learned knowledge to the baseline model. Experimental results show that the proposed method can significantly boost the performance of the baseline model and ranks the $1^{st}$ place among all monocular-based methods on the KITTI benchmark. Besides, extensive ablation studies are conducted, which further prove the effectiveness of each part of our designs and illustrate what the baseline model has learned from the LiDAR Net.
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Authors: Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

We show that simple spatial transformations, namely translations and rotations alone, suffice to fool neural networks on a significant fraction of their inputs in multiple image classification tasks. Our results are in sharp contrast to previous work in adversarial robustness that relied on more complicated optimization ap- proaches unlikely to appear outside a truly adversarial context. Moreover, the misclassifying rotations and translations are easy to find and require only a few black-box queries to the target model. Overall, our findings emphasize the need to design robust classifiers even for natural input transformations in benign settings.
Feature Map Variational Auto-Encoders
Authors: Lars Maaløe, Ole Winther

There have been multiple attempts with variational auto-encoders (VAE) to learn powerful global representations of complex data using a combination of latent stochastic variables and an autoregressive model over the dimensions of the data. However, for the most challenging natural image tasks the purely autoregressive model with stochastic variables still outperform the combined stochastic autoregressive models. In this paper, we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers. We significantly improve the state-of-the-art results on MNIST, OMNIGLOT, CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach. Interestingly, we also observe close to state-of-the-art results without the autoregressive part. This opens the possibility for high quality image generation with only one forward-pass.
Interrogating Paradigms in Self-supervised Graph Representation Learning
Authors: Puja Trivedi, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

Graph contrastive learning (GCL) is a newly popular paradigm for self-supervised graph representation learning and offers an alternative to reconstruction-based methods.However, it is not well understood what conditions a task must satisfy such that a given paradigm is better suited. In this paper, we investigate the role of dataset properties and augmentation strategies on the success of GCL and reconstruction-based approaches. Using the recent population augmentation graph-based analysis of self-supervised learning, we show theoretically that the success of GCL with popular augmentations is bounded by the graph edit distance between different classes. Next, we introduce a synthetic data generation process that systematically controls the amount of style vs. content in each sample- i.e. information that is irrelevant vs. relevant to the downstream task- to elucidate how graph representation learning methods perform under different dataset conditions. We empirically show that reconstruction approaches perform better when the style vs. content ratio is low and GCL with popular augmentations benefits from moderate style. Our results provide a general, systematic framework for analyzing different graph representation learning methods and demonstrate when a given approach is expected to perform well.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
Authors: Xin Liu, Ziheng Jiang, Joshua Wolff Fromm, Xuhai Xu, Shwetak Patel, Daniel McDuff

There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for generating the training target. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations. In this paper, we present a novel meta-learning approach called MetaPhys for learning personalized cardiac signals from 18-seconds of video data. MetaPhys works in both supervised and unsupervised manners. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. Visualization of attention maps and ablation experiments reveal how the model adapts to each subject and why our proposed approach leads to these improvements. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Authors: Arun Venkitaraman, Anders Hansson, Bo Wahlberg

This paper investigates the use of nonparametric kernel-regression to obtain a task- similarity aware meta-learning algorithm. Our hypothesis is that the use of task- similarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks. While existing meta-learning approaches implicitly assume the tasks as being similar, it is generally unclear how this task-similarity could be quantified and used in the learning. As a result, most popular meta- learning approaches do not actively use the similarity/dissimilarity between the tasks, but rely on availability of huge number of tasks for their working. Our contribution is a novel framework for meta-learning that explicitly uses task-similarity in the form of kernels and an associated meta-learning algorithm. We model the task-specific parameters to belong to a reproducing kernel Hilbert space where the kernel function captures the similarity across tasks. The proposed algorithm iteratively learns a meta-parameter which is used to assign a task-specific descriptor for every task. The task descriptors are then used to quantify the task-similarity through the kernel function. We show how our approach conceptually generalizes the popular meta-learning approaches of model-agnostic meta-learning (MAML) and Meta-stochastic gradient descent (Meta-SGD) approaches. Numerical experiments with regression and classification tasks show that our algorithm outperforms these approaches when the number of tasks is limited, even in the presence of out- lier or dissimilar tasks. This supports our hypothesis that task-similarity helps improve the meta-learning performance in task-limited and adverse settings.
Aggregated Momentum: Stability Through Passive Damping
Authors: James Lucas, Shengyang Sun, Richard Zemel, Roger  Grosse

Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient. Largecamping  coefficients can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different damping coefficients. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive damping coefficients such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence with little to no tuning.
Generative Modeling for Multitask Visual Learning
Authors: Zhipeng Bao, Yu-Xiong Wang, Martial Hebert

Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across various visual perception tasks. Correspondingly, we propose a general multi-task oriented generative modeling (MGM) framework, by coupling a discriminative multi-task network with a generative network. While it is challenging to synthesize both RGB images and pixel-level annotations in multi-task scenarios, our framework enables us to use synthesized images paired with only weak annotations (i.e., image-level scene labels) to facilitate multiple visual tasks. Experimental evaluation on challenging multi-task benchmarks, including NYUv2 and Taskonomy, demonstrates that our MGM framework improves the performance of all the tasks by large margins, especially in the low-data regimes, and our model consistently outperforms state-of-the-art multi-task approaches.
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Authors: Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal

While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-world samples. We circumvent this challenge by using additional data from proxy distributions learned by advanced  generative models. We first seek to formally understand the transfer of robustness from classifiers trained on proxy distributions to the real data distribution. We prove that the difference between the robustness of a classifier on the two distributions is upper bounded by the conditional Wasserstein distance between them. Next we use proxy distributions to significantly improve the performance of adversarial training on five different datasets. For example, we improve robust accuracy by up to $7.5$% and $6.7$% in $\ell_{\infty}$ and $\ell_2$ threat model over baselines that are not using proxy distributions on the CIFAR-10 dataset. We also improve certified robust accuracy by $7.6$% on the CIFAR-10 dataset. We further demonstrate that different generative models brings a disparate improvement in the performance in robust training. We propose a robust discrimination approach to characterize the impact and further provide a deeper understanding of why diffusion-based generative models are a better choice for proxy distribution than generative adversarial networks.
Poincare Glove: Hyperbolic Word Embeddings
Authors: Alexandru Tifrea*, Gary Becigneul*, Octavian-Eugen Ganea*

Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy.
Coverage and Quality Driven Training of Generative Image Models
Authors: Thomas LUCAS, Konstantin SHMELKOV, Karteek ALAHARI, Cordelia SCHMID, Jakob VERBEEK

Generative modeling of natural images has been extensively studied in recent years, yielding remarkable progress. Current state-of-the-art methods are either based on maximum likelihood estimation or adversarial training. Both methods have their own drawbacks, which are complementary in nature. The first leads to over-generalization as the maximum likelihood criterion encourages models to cover the support of the training data by heavily penalizing small masses assigned to training data. Simplifying assumptions in such models limits their capacity and makes them spill mass on unrealistic samples. The second leads to mode-dropping since adversarial training encourages high quality samples from the model, but only indirectly enforces diversity among the samples. To overcome these drawbacks we make two contributions. First, we propose a model that extends variational autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space. This induces correlations among the pixels given the latent variables, improving over factorial decoders commonly used in variational autoencoders. Second, we propose a unified training approach that leverages coverage and quality based criteria. Our models obtain likelihood scores competitive with state-of-the-art likelihood-based models, while achieving sample quality typical of adversarially trained networks.
Similarity Search for Efficient Active Learning and Search of Rare Concepts
Authors: Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz

Many active learning and search approaches are intractable for industrial settings with billions of unlabeled examples. Existing approaches, such as uncertainty sampling or information density, search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. However, in practice, data is often heavily skewed; only a small fraction of collected data will be relevant for a given learning task. For example, when identifying rare classes, detecting malicious content, or debugging model performance, positive examples can appear in less than 1% of the data. In this work, we exploit this skew in large training datasets to reduce the number of unlabeled examples considered in each selection round by only looking at the nearest neighbors to the labeled examples. Empirically, we observe that learned representations can effectively cluster unseen concepts, making active learning very effective and substantially reducing the number of viable unlabeled examples. We evaluate several selection strategies in this setting on three large-scale computer vision datasets: ImageNet, OpenImages, and a proprietary dataset of 10 billion images from a large internet company. For rare classes, active learning methods need as little as 0.31% of the labeled data to match the average precision of full supervision. By limiting the selection strategies to the immediate neighbors of the labeled data as candidates for labeling, we process as little as 0.1% of the unlabeled data while achieving similar reductions in labeling costs as the traditional global approach. This process of expanding the candidate pool with the nearest neighbors of the labeled set can be done efficiently and reduces the computational complexity of selection by orders of magnitude.
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
Authors: Chaoyue Liu, Libin Zhu, Misha Belkin

Wide neural networks with linear output layer have been shown to be near-linear, and to have near-constant neural tangent kernel (NTK), in a region containing the optimization path of gradient descent. These findings seem counter-intuitive since in general neural networks are highly complex models. Why does a linear structure emerge when the neural networks become wide? 
In this work, we provide a new perspective on this "transition to linearity" by considering a neural network as an assembly model recursively built from a set of sub-models corresponding to individual neurons. In this view, we show that the linearity of wide neural networks is, in fact, an emerging property of assembling a large number of diverse ``weak'' sub-models, none of which dominate the assembly.
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Authors: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

It is well known that it is possible to construct "adversarial examples"
for neural networks: inputs which are misclassified by the network
yet indistinguishable from true data. We propose a simple
modification to standard neural network architectures, thermometer
encoding, which significantly increases the robustness of the network to
adversarial examples. We demonstrate this robustness with experiments
on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that
models with thermometer-encoded inputs consistently have higher accuracy
on adversarial examples, without decreasing generalization.
State-of-the-art accuracy under the strongest known white-box attack was 
increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.
We explore the properties of these networks, providing evidence
that thermometer encodings help neural networks to
find more-non-linear decision boundaries.
MAGNEx: A Model Agnostic Global Neural Explainer
Authors: Nikolaos Manginas, Prodromos Malakasiotis, Eirini Spyropoulou, Ion Androutsopoulos, Georgios Paliouras

Black-box decision models have been widely adopted both in industry and academia due to their excellent performance across many challenging tasks and domains. However, much criticism has been raised around modern AI systems, to a large extent due to their inability to produce explainable decisions that both their end-users and their developers can trust. The need for such decisions, i.e., decisions accompanied by a rationale for why they are made, has ignited much recent research. We propose MAGNEx, a global algorithm that leverages neural-network based explainers to produce rationales for any black-box decision model, neural or not. MAGNEx is model-agnostic, and thus easily generalizable across domains and applications. More importantly, MAGNEx is global, i.e., it learns to create rationales by optimizing for a number of instances at once, contrary to local methods that aim at explaining a single example. The global nature of MAGNEx has two advantages over local methods: i) it generalizes across instances hence producing more faithful explanations, ii) it is computationally more efficient during inference. Our experiments confirm that MAGNEx outperforms popular explainability algorithms both in explanation quality and in computational efficiency.
Learning to Generalize across Domains on Single Test Samples
Authors: Zehao Xiao, Xiantong Zhen, Ling Shao, Cees G. M. Snoek

We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in the learned model not being explicitly adapted to the unseen target domains. We propose learning to generalize across domains on single test samples. We leverage a meta-learning paradigm to learn our model to acquire the ability of adaptation with single samples at training time so as to further adapt itself to each single test sample at test time. We formulate the adaptation to the single test sample as a variational Bayesian inference problem, which incorporates the test sample as a conditional into the generation of model parameters. The adaptation to each test sample requires only one feed-forward computation at test time without any fine-tuning or self-supervised training on additional data from the unseen domains.  Extensive ablation studies demonstrate that our model learns the ability to adapt models to each single sample by mimicking domain shifts during training. Further, our model achieves at least comparable -- and often better -- performance than state-of-the-art methods on multiple benchmarks for domain generalization.
Intra-class Mixup for Out-of-Distribution Detection
Authors: Deepak Ravikumar, Sangamesh Kodge, Isha Garg, Kaushik Roy

Deep neural networks have found widespread adoption in solving image recognition and natural language processing tasks. However, they make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. Inter-class mixup has been shown to improve model calibration aiding OoD detection. However, we show that both empirical risk minimization and inter-class mixup create large angular spread in latent representation. This reduces the separability of in-distribution data from OoD data. In this paper we propose intra-class mixup supplemented with angular margin to improve OoD detection. Angular margin is the angle between the decision boundary normal and sample representation. We show that intra-class mixup forces the network to learn representations with low angular spread in the latent space. This improves the separability of OoD from in-distribution examples. Our approach when applied to various existing OoD detection techniques shows an improvement of 4.68% and 6.38%  in AUROC  performance over empirical risk minimization and inter-class mixup, respectively. Further, our approach aided with angular margin improves AUROC performance by 7.36% and 9.10%  over empirical risk minimization and inter-class mixup, respectively.
Revisiting Few-sample BERT Fine-tuning
Authors: Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, Yoav Artzi

This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network for down-stream tasks; and the prevalent practice of using a pre-determined, and small number of training iterations. We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process. In light of these observations, we re-visit recently proposed methods to improve few-sample fine-tuning with BERT and re-evaluate their effectiveness. Generally, we observe the impact of these methods diminishes significantly with our modified process.