nils-herrmann/clean_scirepeval huggingface dataset:
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Authors: Xiang Gao, Wei Hu, Guo-Jun Qi

3D object representation learning is a fundamental challenge in computer vision to draw inferences about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly trained in a supervised fashion, which often requires a large amount of data labels with high cost. Hence, it is critical to learn multi-view feature representations in a self-supervised fashion. To this end, we propose a novel self-supervised learning paradigm of Multi-View Transformation Equivariant Representations (MV-TER), exploiting the equivariant transformations of a 3D object and its projected multiple views. Specifically, we perform a 3D transformation on a 3D object, and obtain multiple views before and after transformation via projection. Then, we self-train a representation learning module to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks.
Adaptive Discretization for Continuous Control using Particle Filtering Policy Network
Authors: Pei Xu, Ioannis Karamouzas

Controlling the movements of highly articulated agents and robots has been a long-standing challenge to model-free deep reinforcement learning. In this paper, we propose a simple, yet general, framework for improving the performance of policy gradient algorithms by discretizing the continuous action space. Instead of using a fixed set of predetermined atomic actions, we exploit particle filtering to adaptively discretize actions during training and track the posterior policy represented as a mixture distribution. The resulting policy can replace the original continuous policy of any given policy gradient algorithm without changing its underlying model architecture. We demonstrate the applicability of our approach to state-of-the-art on-policy and off-policy baselines in challenging control tasks. Baselines using our particle-based policies achieve better final performance and speed of convergence as compared to corresponding continuous implementations and implementations that rely on fixed discretization schemes.
ResPerfNet: Deep Residual Learning for Regressional Performance Modeling of Deep Neural Networks
Authors: Chuan-Chi Wang, Ying-Chiao Liao, Chia-Heng Tu, Ming-Chang Kao, Wen-Yew Liang, Shih-Hao Hung

The rapid advancements of computing technology facilitate the development of diverse deep learning applications. Unfortunately, the efficiency of parallel computing infrastructures varies widely with neural network models, which hinders the exploration of the design space to find high-performance neural network architectures on specific computing platforms for a given application. To address such a challenge, we propose a deep learning-based method, ResPerfNet, which trains a residual neural network with representative datasets obtained on the target platform to predict the performance for a deep neural network. Our experimental results show that ResPerfNet can accurately predict the execution time of individual neural network layers and full network models on a variety of platforms. In particular, ResPerfNet achieves 8.4% of mean absolute percentage error for LeNet, AlexNet and VGG16 on the NVIDIA GTX 1080Ti, which is substantially lower than the previously published works.
Model Compression with Generative Adversarial Networks
Authors: Ruishan Liu, Nicolo Fusi, Lester Mackey

More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Model compression (also known as distillation) alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher's training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as deep neural networks and large random forests on both image and tabular datasets. Building on these results, we propose a comprehensive metric—the Compression Score—to evaluate the quality of synthetic datasets based on their induced model compression performance. The Compression Score captures both data diversity and discriminability, and we illustrate its benefits over the popular Inception Score in the context of image classification.
ItNet: iterative neural networks for fast and efficient anytime prediction
Authors: Thomas Pfeil

Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Common requirements are high accuracy, high throughput, low latency, and a small memory footprint. A good trade-off between accuracy and latency has been shown by networks comprising multiple intermediate outputs. In this study, we introduce a multi-output network that has a tiny memory footprint in terms of its computational graph, which allows its execution on novel, massively-parallel hardware accelerators designed for extremely high throughput. To this end, the graph is designed to contain loops by iteratively executing a single network building block. These so-called iterative neural networks enable stateof-the-art results for semantic segmentation on the CamVid and Cityscapes datasets that are especially demanding in terms of computational resources. In ablation studies, the improvement of network training by intermediate network outputs as well as the trade-off between weight sharing over iterations and the network size are investigated.
Policy Optimization via Stochastic Recursive Gradient Algorithm
Authors: Huizhuo Yuan, Chris Junchi Li, Yuhao Tang, Yuren Zhou

In this paper, we propose the StochAstic Recursive grAdient Policy Optimization (SARAPO) algorithm which is a novel variance reduction method on Trust Region Policy Optimization (TRPO). The algorithm incorporates the StochAstic Recursive grAdient algoritHm(SARAH) into the TRPO framework. Compared with the existing Stochastic Variance Reduced Policy Optimization (SVRPO), our algorithm is more stable in the variance. Furthermore, by theoretical analysis the ordinary differential equation and the stochastic differential equation (ODE/SDE) of SARAH, we analyze its convergence property and stability. Our experiments demonstrate its performance on a variety of benchmark tasks. We show that our algorithm gets better improvement in each iteration and matches or even outperforms SVRPO and TRPO.
Video Prediction with Variational Temporal Hierarchies
Authors: Vaibhav Saxena, Jimmy Ba, Danijar Hafner

Deep learning has shown promise for accurately predicting high-dimensional video sequences. Existing video prediction models succeeded in generating sharp but often short video sequences. Toward improving long-term video prediction, we study hierarchical latent variable models with levels that process at different time scales. To gain insights into the representations of such models, we study the information stored at each level of the hierarchy via the KL divergence, predictive entropy, datasets of varying speed, and generative distributions. Our analysis confirms that faster changing details are generally captured by lower levels, while slower changing facts are remembered by higher levels. On synthetic datasets where common methods fail after 25 frames, we show that temporally abstract latent variable models can make accurate predictions for up to 200 frames.
Generalized Capsule Networks with Trainable Routing Procedure
Authors: Zhenhua Chen, Chuhua Wang, Tiancong Zhao, David Crandall

CapsNet (Capsule Network) was first proposed by Sabour et al. (2017) and lateranother version of CapsNet was proposed by Hinton et al. (2018).  CapsNet hasbeen proved effective in modeling spatial features with much fewer parameters.However, the routing procedures (dynamic routing and EM routing) in both pa-pers are not well incorporated into the whole training process,  and the optimalnumber for the routing procedure has to be found manually.  We propose Gen-eralized GapsNet (G-CapsNet) to overcome this disadvantages by incorporatingthe routing procedure into the optimization.  We implement two versions of G-CapsNet (fully-connected and convolutional) on CAFFE (Jia et al. (2014)) andevaluate them by testing the accuracy on MNIST & CIFAR10, the robustness towhite-box & black-box attack, and the generalization ability on GAN-generatedsynthetic images.  We also explore the scalability of G-CapsNet by constructinga relatively deep G-CapsNet.   The experiment shows that G-CapsNet has goodgeneralization ability and scalability.
Learning Kolmogorov Models for Binary Random Variables
Authors: Hadi Ghauch, Hossein S. Ghadikolaei, Mikael Skoglund, Carlo Fischione

We propose a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link (in the sense of implications in mathematical logic) outcomes of specific random variables and extract valuable relations from the data. We also propose an efficient algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We exemplify our general framework to recommendation systems and gene expression data. We believe that the work is a significant step toward interpretable machine learning.
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
Authors: Hyunwoo Jung, Moonsu Han, Minki Kang, Sungju Hwang

Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majority of uninformative entries. We tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as Long-term Episodic Memory Networks (LEMN), that features a RNN-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance. Such learning of retention agent allows our long-term episodic memory network to retain memory entries of generic importance for a given task. We validate our model on a path-finding task as well as synthetic and real question answering tasks, on which our model achieves significant improvements over the memory augmented networks with rule-based memory scheduling as well as an RL-based baseline that does not consider relative or historical importance of the memory.
PROVABLY BENEFITS OF DEEP HIERARCHICAL RL
Authors: Zeyu Jia, Simon S. Du, Ruosong Wang, Mengdi Wang, Lin F. Yang

Modern complex sequential decision-making problem often both low-level policy and high-level planning. Deep hierarchical reinforcement learning (Deep HRL) admits multi-layer abstractions which naturally model the policy in a hierarchical manner, and it is believed that deep HRL can reduce the sample complexity compared to the standard RL frameworks. We initiate the study of rigorously characterizing the complexity of Deep HRL. We present a model-based optimistic algorithm which demonstrates that the complexity of learning a near-optimal policy for deep HRL scales with the sum of number of states at each abstraction layer whereas standard RL scales with the product of number of states at each abstraction layer. Our algorithm achieves this goal by using the fact that distinct high-level states have similar low-level structures, which allows an efficient information exploitation and thus experiences from different high-level state-action pairs can be generalized to unseen state-actions. Overall, our result shows an exponential improvement using Deep HRL comparing to standard RL framework.
Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
Authors: Abhinav Jauhri, Brad Stocks, Jian Hui Li, Koichi Yamada, John Paul Shen

This paper focuses on the synthetic generation of human mobility data in urban areas. We present a novel and scalable application of Generative Adversarial Networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model. Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week. Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets. Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation.
GENERATIVE LOW-SHOT NETWORK EXPANSION
Authors: Adi Hayat, Mark Kliger, Shachar Fleishman, Daniel Cohen-Or

Conventional deep learning classifiers are static in the sense that they are trained on
a predefined set of classes and learning to classify a novel class typically requires
re-training. In this work, we address the problem of Low-shot network-expansion
learning. We introduce a learning framework which enables expanding a pre-trained
(base) deep network to classify novel classes when the number of examples for the
novel classes is particularly small. We present a simple yet powerful distillation
method where the base network is augmented with additional weights to classify
the novel classes, while keeping the weights of the base network unchanged. We
term this learning hard distillation, since we preserve the response of the network
on the old classes to be equal in both the base and the expanded network. We
show that since only a small number of weights needs to be trained, the hard
distillation excels for low-shot training scenarios. Furthermore, hard distillation
avoids detriment to classification performance on the base classes. Finally, we
show that low-shot network expansion can be done with a very small memory
footprint by using a compact generative model of the base classes training data
with only a negligible degradation relative to learning with the full training set.
What classifiers know what they don't know?
Authors: Mohamed Ishmael Belghazi, David Lopez-Paz

Being uncertain when facing the unknown is key to intelligent decision making. However, machine learning algorithms lack reliable estimates about their predictive uncertainty. This leads to wrong and overly-confident decisions when encountering classes unseen during training. Despite the importance of equipping classifiers with uncertainty estimates ready for the real world, prior work has focused on small datasets and little or no class discrepancy between training and testing data. To close this gap, we introduce UIMNET: a realistic, ImageNet-scale test-bed to evaluate predictive uncertainty estimates for deep image classifiers. Our benchmark provides implementations of eight state-of-the-art algorithms, six uncertainty measures, four in-domain metrics, three out-domain metrics, and a fully automated pipeline to train, calibrate, ensemble, select, and evaluate models. Our test-bed is open-source and all of our results are reproducible from a fixed commit in our repository. Adding new datasets, algorithms, measures, or metrics is a matter of a few lines of code-in so hoping that UIMNET becomes a stepping stone towards realistic, rigorous, and reproducible research in uncertainty estimation. Our results show that ensembles of ERM classifiers as well as single MIMO classifiers are the two best alternatives currently available to measure uncertainty about both in-domain and out-domain classe.
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning
Authors: Sasha Abramowitz

This paper investigates the performance of Scalable Evolution Strategies (S-ES) as a Hierarchical Reinforcement Learning (HRL) approach. S-ES, named for its excellent scalability across many processors, was popularised by OpenAI when they showed its performance to be comparable to the state-of-the-art policy gradient methods. However, to date, S-ES has not been tested in conjunction with HRL methods, which empower temporal abstraction thus allowing agents to tackle more challenging problems. In this work, we introduce a novel method that merges S-ES and HRL, which allows S-ES to be applied to difficult problems such as simultaneous robot locomotion and navigation. We show that S-ES needed no (methodological or hyperparameter) modifications for it to be used in a hierarchical context and that its indifference to delayed rewards leads to it having competitive performance with state-of-the-art gradient-based HRL methods. This leads to a novel HRL method that achieves state-of-the-art performance, and is also comparably simple and highly scalable.
For interpolating kernel machines, minimizing the norm of the ERM solution minimizes stability
Authors: Akshay Rangamani, Lorenzo Rosasco, Tomaso Poggio

We study the average CV Leave One Out stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm minimizes a bound on CV Leave One Out stability, which in turn is controlled by the condition number of the empirical kernel matrix. The latter can be characterized in the asymptotic regime where both the dimension and cardinality of the data go to infinity. Under the assumption of random kernel matrices, the corresponding test error should be expected to follow a double descent curve.
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach
Authors: Yingjun Pei, Xinwen Hou

The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms.We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound. Then, we maximize this lower bound with the Stein variational (SV) gradient method. 
We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO). Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly. Finally, we study the information-bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE).
We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process. We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Authors: Mattias Teye, Hossein Azizpour, Kevin Smith

Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framework to handle uncertainty, inference in Bayesian-inspired deep neural networks is difficult. In this paper, we provide a practical approach to Bayesian learning that relies on a regularization technique found in nearly every modern network, batch normalization. We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models, and we demonstrate how this finding allows us to make useful estimates of the model uncertainty. Using our approach, it is possible to make meaningful uncertainty estimates using conventional architectures without modifying the network or the training procedure. Our approach is thoroughly validated in a series of empirical experiments on different tasks and using various measures, showing it to outperform baselines on a majority of datasets with strong statistical significance.
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION
Authors: Abhinav Sagar

Deep learning motivated by convolutional neural networks has been highly successful in a range of medical imaging problems like image classification, image
segmentation, image synthesis etc. However for validation and interpretability, not
only do we need the predictions made by the model but also how confident it is
while making those predictions. This is important in safety critical applications
for the people to accept it. In this work, we used an encoder decoder architecture
based on variational inference techniques for segmenting brain tumour images. We
evaluate our work on the publicly available 
Coefficient (DSC) and Intersection Over Union (IOU) as the evaluation metrics.
Our model is able to segment brain tumours while taking into account both aleatoric
uncertainty and epistemic uncertainty in a principled bayesian manner.
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
Authors: Michael Lingzhi Li, Meng Dong, Jiawei Zhou, Alexander M. Rush

Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding on both how to compare different architectures and how to construct GNNs systematically. Here, we propose a hierarchy of GNNs based on their aggregation regions. We derive theoretical results about the discriminative power and feature representation capabilities of each class. Then, we show how this framework can be utilized to systematically construct arbitrarily powerful GNNs. As an example, we construct a simple architecture that exceeds the expressiveness of the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theory on both synthetic and real-world benchmarks, and demonstrate our example's theoretical power translates to state-of-the-art results on node classification, graph classification, and graph regression tasks.