nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Authors: Yurong You, Carlos Andres Diaz-Ruiz, Yan Wang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger

Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions. 
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies, causing them to fail in new environments---a serious problem if autonomous vehicles are meant to operate freely. In this paper, we propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain, which our method generates while the vehicle is parked, based on replays of previously recorded driving sequences. In these replays, objects are tracked over time, and detections are interpolated and extrapolated---crucially, leveraging future information to catch hard cases. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain-gap to new driving environments, yielding drastic improvements in accuracy and detection reliability.
New Loss Functions for Fast Maximum Inner Product Search
Authors: Ruiqi Guo, Quan Geng, David Simcha, Felix Chern, Phil Sun, Sanjiv Kumar

Quantization based methods are popular for solving large scale maximum inner product search problems. However, in most traditional quantization works, the objective is to minimize the reconstruction error for datapoints to be searched. In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions. One key aspect of the new loss functions is that we weight the error term based on the value of the inner product, giving more importance to pairs of queries and datapoints whose inner products are high. We provide theoretical grounding to the new quantization loss function, which is simple, intuitive and able to work with a variety of quantization techniques, including binary quantization and product quantization. We conduct experiments on public benchmarking datasets \url{http://ann-benchmarks.com} to demonstrate that our method using the new objective outperforms other state-of-the-art methods. We are committed to release our source code.
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
Authors: Anthony Hu, Alex Kendall, Roberto Cipolla

Understanding object motion is one of the core problems in computer vision. It requires segmenting and tracking objects over time. Significant progress has been made in instance segmentation, but such models cannot track objects, and more crucially, they are unable to reason in both 3D space and time.
We propose a new spatio-temporal embedding loss on videos that generates temporally consistent video instance segmentation. Our model includes a temporal network that learns to model temporal context and motion, which is essential to produce smooth embeddings over time. Further, our model also estimates monocular depth, with a self-supervised loss, as the relative distance to an object effectively constrains where it can be next, ensuring a time-consistent embedding. Finally, we show that our model can accurately track and segment instances, even with occlusions and missed detections, advancing the state-of-the-art on the KITTI Multi-Object and Tracking Dataset.
Adapting Behaviour for Learning Progress
Authors: Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero

Determining what experience to generate to best facilitate learning (i.e. exploration) is one of the distinguishing features and open challenges in reinforcement learning. The advent of distributed agents that interact with parallel instances of the environment has enabled larger scale and greater flexibility, but has not removed the need to tune or tailor exploration to the task, because the ideal data for the learning algorithm necessarily depends on its process of learning. We propose to dynamically adapt the data generation by using a non-stationary multi-armed bandit to optimize a proxy of the learning progress. The data distribution is controlled via modulating multiple parameters of the policy (such as stochasticity, consistency or optimism) without significant overhead. The adaptation speed of the bandit can be increased by exploiting the factored modulation structure. We demonstrate on a suite of Atari 2600 games how this unified approach produces results comparable to per-task tuning at a fraction of the cost.
On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport
Authors: Minhui Huang, Shiqian Ma, Lifeng Lai

This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In the discrete distributions case, the EOT problem can be formulated as a linear program (LP). Since this LP is prohibitively large for general LP solvers, Scetbon \etal suggests to perturb the problem by adding an entropy regularization. They proposed a projected alternating maximization algorithm (PAM) to solve the dual of the entropy regularized EOT. In this paper, we provide the first convergence analysis of PAM. A novel rounding procedure is proposed to help construct the primal solution for the original EOT problem. We also propose a variant of PAM by incorporating the extrapolation technique that can numerically improve the performance of PAM. Results in this paper may shed lights on block coordinate (gradient) descent methods for general optimization problems.
Non-robust Features through the Lens of Universal Perturbations
Authors: Sung Min Park, Kuo-An Wei, Kai Yuanqing Xiao, Jerry Li, Aleksander Madry

Recent work ties adversarial examples to existence of non-robust features: features which are susceptible to small perturbations and believed to be unintelligible to humans, but still useful for prediction. We study universal adversarial perturbations and demonstrate that the above picture is more nuanced. Specifically, even though universal perturbations---similarly to standard adversarial perturbations---do leverage non-robust features, these features tend to be fundamentally different from the ``standard'' ones and, in particular, non-trivially human-aligned. Namely, universal perturbations have more human-aligned locality and spatial invariance properties. However, we also show that these human-aligned non-robust features have much less predictive signal than general non-robust features. Our findings thus take a step towards improving our understanding of these previously unintelligible features.
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Authors: Tom Le Paine, Sergio Gomez, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a  task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task.
The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.
Soteria: In search of efficient neural networks for private inference
Authors: Anshul Aggarwal, Trevor E Carlson, Reza Shokri, Shruti Tople

In the context of ML as a service, our objective is to protect the confidentiality of the users’ queries and the server's model parameters, with modest computation and communication overhead. Prior solutions primarily propose fine-tuning cryptographic methods to make them efficient for known fixed model architectures. The drawback with this line of approach is that the model itself is never designed to efficiently operate with existing cryptographic computations. We observe that the network architecture, internal functions, and parameters of a model, which are all chosen during training, significantly influence the computation and communication overhead of a cryptographic method, during inference.Thus, we propose SOTERIA — a training method to construct model architectures that are by-design efficient for private inference. We use neural architecture search algorithms with the dual objective of optimizing the accuracy of the model and the overhead of using cryptographic primitives for secure inference. Given the flexibility of modifying a model during training, we find accurate models that are also efficient for private computation. We select garbled circuits as our underlying cryptographic primitive, due to their expressiveness and efficiency. We empirically evaluate SOTERIA on MNIST and CIFAR10 datasets, to compare with the prior work on secure inference. Our results confirm that SOTERIA is indeed effective in balancing performance and accuracy.
End-to-end learning of pharmacological assays from high-resolution microscopy images
Authors: Markus Hofmarcher, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer

Predicting the outcome of pharmacological assays based on high-resolution microscopy
images of treated cells is a crucial task in drug discovery which tremendously
increases discovery rates. However, end-to-end learning on these images
with convolutional neural networks (CNNs) has not been ventured for this task
because it has been considered infeasible and overly complex. On the largest
available public dataset, we compare several state-of-the-art CNNs trained in an
end-to-end fashion with models based on a cell-centric approach involving segmentation.
We found that CNNs operating on full images containing hundreds
of cells perform significantly better at assay prediction than networks operating
on a single-cell level. Surprisingly, we could predict 29% of the 209 pharmacological
assays at high predictive performance (AUC > 0.9). We compared a
novel CNN architecture called “GapNet” against four competing CNN architectures
and found that it performs on par with the best methods and at the same time
has the lowest training time. Our results demonstrate that end-to-end learning on
high-resolution imaging data is not only possible but even outperforms cell-centric
and segmentation-dependent approaches. Hence, the costly cell segmentation and
feature extraction steps are not necessary, in fact they even hamper predictive performance.
Our work further suggests that many pharmacological assays could
be replaced by high-resolution microscopy imaging together with convolutional
neural networks.
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods
Authors: Heejae Kim, Taewoo Kim, Chan-Hyun Youn

Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
Authors: Kyra Gan, Su Jia, Andrew A Li

In the problem of \emph{active sequential hypothesis testing} (ASHT), a learner seeks to identify the \emph{true} hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $\delta>0$, the goal is to sequentially select the fewest number of actions so as to identify the true hypothesis with probability at least $1 - \delta$. Motivated by applications in which the number of hypotheses or actions is massive (e.g., genomics-based cancer detection), we propose efficient (greedy, in fact) algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Both of our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data, demonstrating that our algorithms outperform previously proposed heuristic policies by large margins.
Nested Graph Neural Networks
Authors: Muhan Zhang, Pan Li

Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a center node, both 1-WL and GNN obtain a node representation that encodes a rooted subtree around the center node. These rooted subtree representations are then pooled into a single representation to represent the whole graph. However, rooted subtrees are of limited expressiveness to represent a non-tree graph. To address it, we propose Nested Graph Neural Networks (NGNNs). NGNN represents a graph with rooted subgraphs instead of rooted subtrees, so that two graphs sharing many identical subgraphs (rather than subtrees) tend to have similar representations. The key is to make each node representation encode a subgraph around it more than a subtree. To achieve this, NGNN extracts a local subgraph around each node and applies a base GNN to each subgraph to learn a subgraph representation. The whole-graph representation is then obtained by pooling these subgraph representations. We provide a rigorous theoretical analysis showing that NGNN is strictly more powerful than 1-WL. In particular, we proved that NGNN can discriminate almost all r-regular graphs, where 1-WL always fails. Moreover, unlike other more powerful GNNs, NGNN only introduces a constant-factor higher time complexity than standard GNNs. NGNN is a plug-and-play framework that can be combined with various base GNNs. We test NGNN with different base GNNs on several benchmark datasets. NGNN uniformly improves their performance and shows highly competitive performance on all datasets.
Modular Gaussian Processes for Transfer Learning
Authors: Pablo Moreno-Muñoz, Antonio Artés, Mauricio A Álvarez

We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, each model being characterised by its hyperparameters, pseudo-inputs and their corresponding posterior densities, one could build ensemble GP models without revisiting any data. Our method avoids undesired data centralisation, reduces rising computational costs and allows the transfer of learned uncertainty metrics after training. We exploit the augmentation of high-dimensional integral operators based on the Kullback-Leibler divergence between stochastic processes to introduce an efficient lower bound under all the sparse variational GPs, with different complexity and even likelihood distribution. The method is also valid for multi-output GPs, learning correlations a posteriori between independent modules. Extensive results illustrate the usability of our framework in large-scale and multi-task experiments, also compared with the exact inference methods in the literature.
Robust Auction Design in the Auto-bidding World
Authors: Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

In classic auction theory, reserve prices are known to be effective for improving revenue for the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve prices, however, usually do not help improve total welfare of the auctioneer and the bidders. In this paper, we focus on value maximizing bidders with return on spend constraints---a paradigm that has drawn considerable attention recently as more advertisers adopt auto-bidding algorithms in advertising platforms---and show that the introduction of reserve prices has a novel impact on the market. Namely, by choosing reserve prices appropriately the auctioneer can improve not only the total revenue but also the total welfare. Our results also demonstrate that reserve prices are robust to bidder types, i.e., reserve prices work well for different bidder types, such as value maximizers and utility maximizers, without using bidder type information. We generalize these results for a variety of auction mechanisms such as VCG, GSP, and first-price auctions. Moreover, we show how to combine these results with additive boosts to improve the welfare of the outcomes of the auction further. Finally, we complement our theoretical observations with an empirical study confirming the effectiveness of these ideas using data from online advertising auctions.
Differentiable Sparsification for Deep Neural Networks
Authors: Yongjin Lee

Deep neural networks have relieved the feature engineering burden on human experts. However, comparable efforts are instead required to determine an effective architecture. In addition, as the sizes of networks have over-grown, a considerable amount of resources is also invested in reducing the sizes. The sparsification of an over-complete model addresses these problems as it removes redundant parameters or connections. In this study, we propose a fully differentiable sparsification method for deep neural networks, which allows parameters to be zero during training with the stochastic gradient descent. Thus, the proposed method can simultaneously learn the sparsified structure and weights of networks in an end-to-end manner, which can be directly applies to modern deep neural networks and imposes minimum overhead to the training process. To the authors' best knowledge, it is the first fully [sub-]differentiable sparsification method that zeroes out components, and it provides a foundation for future structure learning and model compression methods.
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Authors: Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes. To address this issue, we propose Curriculum Pseudo Labeling (CPL), a curriculum learning approach to leverage unlabeled data according to the model's learning status. The core of CPL is to flexibly adjust thresholds for different classes at each time step to let pass informative unlabeled data and their pseudo labels. CPL does not introduce additional parameters or computations (forward or backward propagation). We apply CPL to FixMatch and call our improved algorithm FlexMatch. FlexMatch achieves state-of-the-art performance on a variety of SSL benchmarks, with especially strong performances when the labeled data are extremely limited or when the task is challenging. For example, FlexMatch achieves 13.96% and 18.96% error rate reduction over FixMatch on CIFAR-100 and STL-10 datasets respectively, when there are only 4 labels per class. CPL also significantly boosts the convergence speed, e.g., FlexMatch can use only 1/5 training time of FixMatch to achieve even better performance. Furthermore, we show that CPL can be easily adapted to other SSL algorithms and remarkably improve their performances. We open-source our code at https://github.com/TorchSSL/TorchSSL.
Understanding Instance-based Interpretability of Variational Auto-Encoders
Authors: Zhifeng Kong, Kamalika Chaudhuri

Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict. However, instance-based interpretations remain ill-understood in the context of unsupervised learning. In this paper, we investigate influence functions [Koh and Liang, 2017], a popular instance-based interpretation method, for a class of deep generative models called variational auto-encoders (VAE). We formally frame the counter-factual question answered by influence functions in this setting, and through theoretical analysis, examine what they reveal about the impact of training samples on classical unsupervised learning methods. We then introduce VAE- TracIn, a computationally efficient and theoretically sound solution based on Pruthi et al. [2020], for VAEs. Finally, we evaluate VAE-TracIn on several real world datasets with extensive quantitative and qualitative analysis.
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Authors: Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu

Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogonality of the weights helps train deep models and improve robustness. For ConvNets, most existing methods are based on penalizing or normalizing weight matrices derived from concatenating or flattening the convolutional kernels. These methods often destroy or ignore the benign convolutional structure of the kernels; therefore, they are often expensive or impractical for deep ConvNets. In contrast, we introduce a simple and efficient ``Convolutional Normalization'' (ConvNorm) method that can fully exploit the convolutional structure in the Fourier domain and serve as a simple plug-and-play module to be conveniently incorporated into any ConvNets. Our method is inspired by recent work on preconditioning methods for convolutional sparse coding and can effectively promote each layer's channel-wise isometry. Furthermore, we show that our ConvNorm can reduce the layerwise spectral norm of the weight matrices and hence improve the Lipschitzness of the network, leading to easier training and improved robustness for deep ConvNets. Applied to classification under noise corruptions and generative adversarial network (GAN), we show that the ConvNorm improves the robustness of common ConvNets such as ResNet and the performance of GAN. We verify our findings via numerical experiments on CIFAR and ImageNet. Our implementation is available online at \url{https://github.com/shengliu66/ConvNorm}.