nils-herrmann/clean_scirepeval huggingface dataset:
MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter Pruning
Authors: Raden Mu'az Mun'im, Jie Lin, Vijay Chandrasekhar, Koichi Shinoda

In this paper, we propose a framework, called MaskConvNet, for ConvNets filter pruning. MaskConvNet provides elegant support for training budget-aware pruned networks from scratch, by adding a simple mask module to a ConvNet architecture. MaskConvNet enjoys several advantages - (1) Flexible, the mask module can be integrated with any ConvNets in a plug-and-play manner. (2) Simple, the mask module is implemented by a hard Sigmoid function with a small number of trainable mask variables, adding negligible memory and computational overheads to the networks during training. (3) Effective, it is able to achieve competitive pruning rate while maintaining comparable accuracy with the baseline ConvNets without pruning, regardless of the datasets and ConvNet architectures used. (4) Fast, it is observed that the number of training epochs required by MaskConvNet is close to training a baseline without pruning. (5) Budget-aware, with a sparsity budget on target metric (e.g. model size and FLOP), MaskConvNet is able to train in a way that the optimizer can adaptively sparsify the network and automatically maintain sparsity level, till the pruned network produces good accuracy and fulfill the budget constraint simultaneously. Results on CIFAR-10 and ImageNet with several ConvNet architectures show that MaskConvNet works competitively well compared to previous pruning methods, with budget-constraint well respected. Code is available at https://www.dropbox.com/s/c4zi3n7h1bexl12/maskconv-iclr-code.zip?dl=0. We hope MaskConvNet, as a simple and general pruning framework, can address the gaps in existing literate and advance future studies to push the boundaries of neural network pruning.
A theoretical framework for deep and locally connected ReLU network
Authors: Yuandong Tian

Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success. In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity. The framework bridges data distribution with gradient descent rules, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm, after a novel discovery of its projection nature. The framework is built upon teacher-student setting, by projecting the student's forward/backward pass onto the teacher's computational graph. We do not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc). Our framework could help facilitate theoretical analysis of many practical issues, e.g. disentangled representations in deep networks.
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks
Authors: Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel

We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks. We provide convergence guarantees with rates for deep linear networks for both continuous and discrete dynamics. Additionally, we study incremental learning phenomena for shallow linear networks. Interestingly, certain specific initializations imply that negligible components are learned {before} the principal ones, thus potentially negatively affecting the effectiveness of such a learning algorithm; a phenomenon we classify as implicit anti-regularization. We also provide initialization schemes where the components of the problem are approximately learned by decreasing order of importance, thus providing a form of implicit regularization.
Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
Authors: Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque

Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training
Authors: Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, qiang liu, Vikas Chandra

Designing accurate and efficient vision transformers (ViTs) is a highly important but challenging task. Supernet-based one-shot neural architecture search (NAS) enables fast architecture optimization and has achieved state-of-the-art (SOTA) results on convolutional neural networks (CNNs). However, directly applying the supernet-based NAS to optimize ViTs leads to poor performance - even worse compared to training single ViTs. In this work, we observe that the poor performance is due to a gradient conflict issue: the gradients of different sub-networks conflict with that of the supernet more severely in ViTs than CNNs, which leads to early saturation in training and inferior convergence. To alleviate this issue, we propose a series of techniques, including a gradient projection algorithm, a switchable layer scaling design, and a simplified data augmentation and regularization training recipe. The proposed techniques significantly improve the convergence and the performance of all sub-networks. Our discovered hybrid ViT model family, dubbed NASViT, achieves top-1 accuracy from 78.2% to 81.8% on ImageNet from 200M to 800M FLOPs, and outperforms all the prior art CNNs and ViTs, including AlphaNet and LeViT, etc. When transferred to semantic segmentation tasks, NASViTs also outperform previous backbones on both Cityscape and ADE20K datasets, achieving 73.2% and 37.9% mIoU with only 5G FLOPs, respectively. Code is available at
https://github.com/facebookresearch/NASViT.
DeepV2D: Video to Depth with Differentiable Structure from Motion
Authors: Zachary Teed, Jia Deng

We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.  DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth.
What does it take to generate natural textures?
Authors: Ivan Ustyuzhaninov *, Wieland Brendel *, Leon Gatys, Matthias Bethge

Natural image generation is currently one of the most actively explored fields in Deep Learning. Many approaches, e.g. for state-of-the-art artistic style transfer or natural texture synthesis, rely on the statistics of hierarchical representations in supervisedly trained deep neural networks. It is, however, unclear what aspects of this feature representation are crucial for natural image generation: is it the depth, the pooling or the training of the features on natural images? We here address this question for the task of natural texture synthesis and show that none of the above aspects are indispensable. Instead, we demonstrate that natural textures of high perceptual quality can be generated from networks with only a single layer, no pooling and random filters.
Prior Networks for Detection of Adversarial Attacks
Authors: Andrey Malinin, Mark Gales

Adversarial examples are considered a serious issue for safety critical applications of AI,  such as finance, autonomous vehicle control and medicinal applications. Though significant work has resulted in increased robustness of systems to these attacks, systems are still vulnerable to well-crafted attacks. To address this problem
several adversarial attack detection methods have been proposed. However, system can still be vulnerable to adversarial samples that are designed to specifically evade these detection methods. One recent detection scheme that has shown good performance is based on uncertainty estimates derived from Monte-Carlo dropout ensembles. Prior Networks, a new method of estimating predictive uncertainty, have been shown to outperform Monte-Carlo dropout on a range of tasks. One of the advantages of this approach is that the behaviour of a Prior Network can be explicitly tuned to, for example, predict high uncertainty in regions where there are no training data samples. In this work Prior Networks are applied to adversarial attack detection using measures of uncertainty in a similar fashion to Monte-Carlo Dropout. Detection based on measures of uncertainty derived from DNNs and Monte-Carlo dropout ensembles are used as a baseline. Prior Networks are shown to significantly out-perform these baseline approaches over a range of adversarial attacks in both detection of whitebox and blackbox configurations. Even when the adversarial attacks are constructed with full knowledge of the detection mechanism, it is shown to be highly challenging to successfully generate an adversarial sample.
Inductive representation learning on temporal graphs
Authors: da Xu, chuanwei ruan, evren korpeoglu, sushant kumar, kannan achan

Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should represent both the static node features and the evolving topological structures. Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture. We propose the temporal graph attention (TGAT) layer to efficiently aggregate temporal-topological neighborhood features to learn the time-feature interactions. For TGAT, we use the self-attention mechanism as building block and develop a novel functional time encoding technique based on the classical Bochner's theorem from harmonic analysis. By stacking TGAT layers, the network recognizes the node embeddings as functions of time and is able to inductively infer embeddings for both new and observed nodes as the graph evolves. The proposed approach handles both node classification and link prediction task, and can be naturally extended to include the temporal edge features. We evaluate our method with transductive and inductive tasks under temporal settings with two benchmark and one industrial dataset. Our TGAT model compares favorably to state-of-the-art baselines as well as the previous temporal graph embedding approaches.
Meta-Q-Learning
Authors: Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola

This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory. Second, a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies. Third, past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates. MQL draws upon ideas in propensity estimation to do so and thereby amplifies the amount of available data for adaptation. Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with the state of the art in meta-RL.
Dynamic Tensor Rematerialization
Authors: Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock

Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current checkpointing techniques statically plan these recomputations offline and assume static computation graphs. We demonstrate that a simple online algorithm can achieve comparable performance by introducing Dynamic Tensor Rematerialization (DTR), a greedy online algorithm for checkpointing that is extensible and general, is parameterized by eviction policy, and supports dynamic models. We prove that DTR can train an $N$-layer linear feedforward network on an  $\Omega(\sqrt{N})$ memory budget with only $\mathcal{O}(N)$ tensor operations. DTR closely matches the performance of optimal static checkpointing in simulated experiments. We incorporate a DTR prototype into PyTorch merely by interposing on tensor allocations and operator calls and collecting lightweight metadata on tensors.
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Authors: Rabeeh Karimi mahabadi, Yonatan Belinkov, James Henderson

While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource scenarios. Since such models are general-purpose feature extractors, many of these features are inevitably irrelevant for a given target task.  We propose to use Variational Information Bottleneck (VIB) to suppress irrelevant features when fine-tuning on low-resource target tasks, and show that our method successfully reduces overfitting.  Moreover, we show that our VIB model finds sentence representations that are more robust to biases in natural language inference datasets, and thereby obtains better generalization to out-of-domain datasets. Evaluation on seven low-resource datasets in different tasks shows that our method significantly improves transfer learning in low-resource scenarios, surpassing prior work. Moreover, it improves generalization on 13 out of 15 out-of-domain natural language inference benchmarks.  Our code is publicly available in https://github.com/rabeehk/vibert.
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
Authors: Dongyang Zhao, Yue Huang, Changnan Xiao, Yue Li, Shihong Deng

Deep reinforcement learning algorithms aim to achieve human-level intelligence by solving practical decisions-making problems, which are often composed of multiple sub-tasks. Complex and subtle relationships between sub-tasks make traditional methods hard to give a promising solution. We implement a first-person shooting environment with random spatial structures to illustrate a typical representative of this kind. A desirable agent should be capable of balancing between different sub-tasks: navigation to find enemies and shooting to kill them. To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients. The proposed framework is able to disentangle multiple sub-tasks and discover proper low-level policies under different situations. The effectiveness and efficiency of the framework are shown by a series of comparison experiments. Both environment and algorithm code will be provided for open source to encourage further research.
CAQL: Continuous Action Q-Learning
Authors: Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier

Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such as
games and recommender systems (RSs). When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient. 
However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem. While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation. Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard. In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers. In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution. To speed up training of CAQL, we develop three techniques, namely (i) dynamic tolerance, (ii) dual filtering, and (iii) clustering.
To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy.
To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.
Learning Corresponded Rationales for Text Matching
Authors: Mo Yu, Shiyu Chang, Tommi S Jaakkola

The ability to predict matches between two sources of text has a number of applications including natural language inference (NLI) and question answering (QA). While flexible neural models have become effective tools in solving these tasks, they are rarely transparent in terms of the mechanism that mediates the prediction. In this paper, we propose a self-explaining architecture where the model is forced to highlight, in a dependent manner, how spans of one side of the input match corresponding segments of the other side in order to arrive at the overall decision. The text spans are regularized to be coherent and concise, and their correspondence is captured explicitly. The text spans -- rationales -- are learned entirely as latent mechanisms, guided only by the distal supervision from the end-to-end task. We evaluate our model on both NLI and QA using three publicly available datasets.  Experimental results demonstrate quantitatively and qualitatively that our method delivers interpretable justification of the prediction without sacrificing state-of-the-art performance. Our code and data split will be publicly available.
Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation 
Authors: Yeabsira Asefa Ashengo, Rosa Tsegaye Aga, Surafel Lemma Abebe

The current approaches for machine translation usually require large set of parallel corpus in order to achieve fluency like in the case of neural machine translation (NMT), statistical machine translation (SMT) and example-based machine translation (EBMT). The context awareness of phrase-based machine translation (PBMT) approaches is also questionable. This research develops a system that translates English text to Amharic text using a combination of context based machine translation (CBMT) and a recurrent neural network machine translation (RNNMT). We built a bilingual dictionary for the CBMT system to use along with a large target corpus. The RNNMT model has then been provided with the output of the CBMT and a parallel corpus for training. Our combinational approach on English-Amharic language pair yields a performance improvement over the simple neural machine translation (NMT).
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
Authors: Yixiao Ge, Dapeng Chen, Hongsheng Li

Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.  In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conventional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.
FitVid: High-Capacity Pixel-Level Video Prediction
Authors: Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain. There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of fitting the common benchmarks so well that it begins to suffer from overfitting -- while having similar parameter count as the current state-of-the-art models. We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques. As a result, FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.