nils-herrmann/clean_scirepeval huggingface dataset:
Rating Continuous Actions in Spatial Multi-Agent Problems
Authors: Uwe Dick, Maryam Tavakol, Ulf Brefeld

We study credit assignment problems in spatial multi-agent environments where agents pursue a joint objective. On the example of soccer, we rate the movements of individual players with respect to their potential for staging a successful attack. We propose a purely data-driven approach to simultaneously learn a model of agent movements as well as their ratings via an agent-centric deep reinforcement learning framework. Our model allows for efficient learning and sampling of ratings in the continuous action space. We empirically observe on historic soccer data that the model accurately rates agent movements w.r.t. their relative contribution to the collective goal.
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
Authors: Thao Nguyen, Maithra Raghu, Simon Kornblith

A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effects of depth and width on the learned representations. In this paper, we study this fundamental question. We begin by investigating how varying depth and width affects model hidden representations, finding a characteristic block structure in the hidden representations of larger capacity (wider or deeper) models. We demonstrate that this block structure arises when model capacity is large relative to the size of the training set, and is indicative of the underlying layers preserving and propagating the dominant principal component of their representations. This discovery has important ramifications for features learned by different models, namely, representations outside the block structure are often similar across architectures with varying widths and depths, but the block structure is unique to each model. We analyze the output predictions of different model architectures, finding that even when the overall accuracy is similar, wide and deep models exhibit distinctive error patterns and variations across classes.
Complex-valued deep learning with differential privacy
Authors: Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis

We present $\zeta$-DP, an extension of differential privacy (DP) to complex-valued functions. After introducing the complex Gaussian mechanism, whose properties we characterise in terms of $(\varepsilon, \delta)$-DP and Rényi-DP, we present $\zeta$-DP stochastic gradient descent ($\zeta$-DP-SGD), a variant of DP-SGD for training complex-valued neural networks. We experimentally evaluate $\zeta$-DP-SGD on three complex-valued tasks, i.e. electrocardiogram classification, speech classification and magnetic resonance imaging (MRI) reconstruction. Moreover, we provide $\zeta$-DP-SGD benchmarks for a large variety of complex-valued activation functions and on a complex-valued variant of the MNIST dataset. Our experiments demonstrate that DP training of complex-valued neural networks is possible with rigorous privacy guarantees and excellent utility.
Diagnosing the Environment Bias in Vision-and-Language Navigation
Authors: Yubo Zhang, Hao Tan, Mohit Bansal

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are extremely useful in navigating new environments which the agent does not know about previously. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons of this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations which contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gap between seen and unseen on multiple datasets (i.e., 8.6% to 0.2% on R2R, 23.9% to 0.1% on R4R, and 3.74 to 0.17 on CVDN) and achieve competitive unseen results to previous state-of-the-art models.
Variational Recurrent Models for Solving Partially Observable Control Tasks
Authors: Dongqi Han, Kenji Doya, Jun Tani

In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorithm for solving PO tasks. Our method comprises two parts: a variational recurrent model (VRM) for modeling the environment, and an RL controller that has access to both the environment and the VRM. The proposed algorithm was tested in two types of PO robotic control tasks, those in which either coordinates or velocities were not observable and those that require long-term memorization. Our experiments show that the proposed algorithm achieved better data efficiency and/or learned more optimal policy than other alternative approaches in tasks in which unobserved states cannot be inferred from raw observations in a simple manner.
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Authors: Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and flexible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensive experiments on several benchmarks verify that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. The effect of DropEdge on preventing over-smoothing is empirically visualized and validated as well. Codes are released on~https://github.com/DropEdge/DropEdge.
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
Authors: Lun Wang, Iosif Pinelis, Dawn Song

We prove that $\mathbb{F}_p$ sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when $p\in(0, 1]$. $\mathbb{F}_p$ sketch uses only polylogarithmic space, exponentially better than existing DP baselines and only worse than the optimal non-private baseline by a logarithmic factor. The evaluation shows that $\mathbb{F}_p$ sketch can achieve reasonable accuracy with strong privacy guarantees. The code for evaluation is included in the supplementary material.
An Information-Theoretic Metric of Transferability for Task Transfer Learning
Authors: Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Amir R. Zamir, Leonidas J. Guibas

An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems. Inspired by a principled information theoretic approach, H-score has a direct connection to the asymptotic error probability of the decision function based on the transferred feature. This formulation of transferability can further be used to select a suitable set of source tasks in task transfer learning problems or to devise efficient transfer learning policies. Experiments using both synthetic and real image data show that not only our formulation of transferability is meaningful in practice, but also it can generalize to inference problems beyond classification, such as recognition tasks for 3D indoor-scene understanding.
A New Perspective on Fluid Simulation: An Image-to-Image Translation Task via Neural Networks
Authors: Roman Lehmann, Markus Hoffmann, Simon Leufen, Wolfgang Karl

Standard numerical methods for creating simulation models in the field of fluid dynamics are designed to be close to perfection, which results in high computational effort and high computation times in many cases. Unfortunately, there is no mathematical way to decrease this correctness in cases where only approximate predictions are needed. For such cases, we developed an approach based on Neural Networks that is much less time-consuming but nearly as accurate as the numerical model for a human observer. We show that we can keep our results stable and nearly indistinguishable from their numerical counterparts over tenth to hundreds of time steps.
Learning Underlying Physical Properties From Observations For Trajectory Prediction
Authors: Ekaterina Nikonova, Jochen Renz

In this work we present an approach that combines deep learning together with
laws of Newton’s physics for accurate trajectory predictions in physical games.
Our model learns to estimate physical properties and forces that generated given
observations, learns the relationships between available player’s actions and estimated
physical properties and uses these extracted forces for predictions. We
show the advantages of using physical laws together with deep learning by evaluating
it against two baseline models that automatically discover features from
the data without such a knowledge. We evaluate our model abilities to extract
physical properties and to generalize to unseen trajectories in two games with a
shooting mechanism. We also evaluate our model capabilities to transfer learned
knowledge from a 2D game for predictions in a 3D game with a similar physics.
We show that by using physical laws together with deep learning we achieve a better
human-interpretability of learned physical properties, transfer of knowledge to
a game with similar physics and very accurate predictions for previously unseen
data.
Once Quantized for All: Progressively Searching for Quantized Compact Models
Authors: Mingzhu Shen, Feng Liang, Chuming Li, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang

Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention. However, the existing quantization-aware Neural Architecture Search (NAS) approaches inherit a two-stage search-retrain schema, which is not only time-consuming but also adversely affected by the unreliable ranking of architectures during the search. To avoid the undesirable effect of the search-retrain schema, we present Once Quantized for All (OQA), a novel framework that searches for quantized compact models and deploys their quantized weights at the same time without additional post-process. While supporting a huge architecture search space, our OQA can produce a series of quantized compact models under ultra-low bit-widths(e.g. 4/3/2 bit). A progressive bit inheritance procedure is introduced to support ultra-low bit-width. Our searched model family, OQANets, achieves a new state-of-the-art (SOTA) on quantized compact models compared with various quantization methods and bit-widths. In particular, OQA2bit-L achieves 64.0\% ImageNet Top-1 accuracy, outperforming its 2 bit counterpart EfficientNet-B0@QKD by a large margin of 14\%  using 30\% less computation cost.
Ergodic Measure Preserving Flows
Authors: Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani

Training probabilistic models with neural network components is intractable in most cases and requires to use approximations such as Markov chain Monte Carlo (MCMC), which is not scalable and requires significant hyper-parameter tuning, or mean-field variational inference (VI), which is biased. While there has been attempts at combining both approaches, the resulting methods have some important limitations in theory and in practice. As an alternative, we propose a novel method which is scalable, like mean-field VI, and, due to its theoretical foundation in ergodic theory, is also asymptotically accurate, like MCMC. We test our method on popular benchmark problems with deep generative models and Bayesian neural networks. Our results show that we can outperform existing approximate inference methods.
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime
Authors: Yicun Duan, Xiangjun Peng

Recent advances in Deep Neural Networks (DNN) compression (e.g. pruning, quantization and etc.) significantly reduces the amount of space consumption for storage, making them easier to deploy in low-cost devices. However, those techniques do not keep the compressed representation during inference runtime, which incurs significant overheads in terms of both performance and space consumption. We introduce ``Succinct Compression”, a three-stage framework to enable DNN inference with near-optimal compression and much better performance during inference runtime. The key insight of our method leverages the concept of \textit{Succinct Data Structures}, which supports fast queries directly on compressed representation without decompression. Our method first transforms DNN models as our proposed formulations in either Element-wise or Block-wise manner, so that \textit{Succinct Data Structures} can take advantage of. Then, our method compresses transformed DNN models using \textit{Succinct Data Structures}. Finally, our method exploits our specialized execution pipelines for different model formulations, to retrieve relevant data for DNN inference. Our experimental results show that, our method keeps near-optimal compression, and achieves at least 8.7X/11.5X speedup on AlexNet/VGG-16 inference, compared with Huffman Coding. We also experimentally show that our method is quite synergistic with Pruning and Quantization.
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation
Authors: Teodor Anders Fredriksson, David Issa Mattos, Jan Bosch, Helena Holmström Olsson

The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is no research that investigates how these algorithms compare to each other in different types of datasets and with different percentages of available labels. To address this problem, this paper empirically evaluates and compares seven algorithms for automated labeling in terms of accuracy. We investigate how these algorithms perform in six different and well-known datasets with three different types of data, images, texts, and numerical values. We evaluate these algorithms under two different experimental conditions, with 10\% and 50\% labels of available labels in the dataset. Each algorithm, in each dataset for each experimental condition, is evaluated independently ten times with different random seeds. The results are analyzed and the algorithms are compared utilizing a Bayesian Bradley-Terry model. The results indicate that while the algorithms label spreading with K-nearest neighbors perform better in the aggregated results, the active learning algorithms query by instance QBC and query instance uncertainty sample perform better when there is only 10\% of labels available. These results can help machine learning practitioners in choosing optimal machine learning algorithms to label their data.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
Authors: Weirui Ye, Pieter Abbeel, Yang Gao

One of the most important AI research questions is to trade off computation versus performance, since "perfect rational" exists in theory but it is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant improvement of performance in varieties of challenging domains. However, the expensive time cost during search severely restricts its scope for applications. This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that mimics the human behavior that spends adequate amounts of time to think about different questions. Inspired by this, we propose a strategy that converges to the ground truth MCTS search results with much less computation. We give theoretical bounds of the V-MCTS and evaluate the performance in $9 \times 9$ Go board games and Atari games. Experiments show that our method can achieve similar performances as the original search algorithm while requiring less than $50\%$ number of search times on average.
We believe that this approach is a viable alternative for tasks with limited time and resources.
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang

We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer by layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer by layer in a cascade manner. As a result, it leads to the best performance on the MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. Code is available at \url{https://github.com/IDEA-opensource/DAB-DETR}.
Efficient Calculation of Polynomial Features on Sparse Matrices
Authors: Andrew Nystrom, John Hughes

We provide an algorithm for polynomial feature expansion that both operates on
and produces a compressed sparse row matrix without any densification. For a
vector of dimension D, density d, and degree k the algorithm has time complexity
O(d^k * D^k) where k is the polynomial-feature order; this is an improvement by a factor d^k
over the standard method.
Intelligent Matrix Exponentiation
Authors: Thomas Fischbacher, Iulia Maria Comsa, Krzysztof Potempa, Moritz Firsching, Luca Versari, Jyrki Alakuijala

We present a novel machine learning architecture that uses a single high-dimensional nonlinearity consisting of the exponential of a single input-dependent matrix. The mathematical simplicity of this architecture allows a detailed analysis of its behaviour, providing robustness guarantees via Lipschitz bounds. Despite its simplicity, a single matrix exponential layer already provides universal approximation properties and can learn and extrapolate fundamental functions of the input, such as periodic structure or geometric invariants. This architecture outperforms other general-purpose architectures on benchmark problems, including CIFAR-10, using fewer parameters.
AlphaZero-based Proof Cost Network to Aid Game Solving
Authors: Ti-Rong Wu, Chung-Chin Shih, Ting Han Wei, Meng-Yu Tsai, Wei-Yuan Hsu, I-Chen Wu

The AlphaZero algorithm learns and plays games without hand-crafted expert knowledge. However, since its objective is to play well, we hypothesize that a better objective can be defined for the related but separate task of solving games. This paper proposes a novel approach to solving problems by modifying the training target of the AlphaZero algorithm, such that it prioritizes solving the game quickly, rather than winning. We train a Proof Cost Network (PCN), where proof cost is a heuristic that estimates the amount of work required to solve problems. This matches the general concept of the so-called proof number from proof number search, which has been shown to be well-suited for game solving. We propose two specific training targets. The first finds the shortest path to a solution, while the second estimates the proof cost. We conduct experiments on solving 15x15 Gomoku and 9x9 Killall-Go problems with both MCTS-based and FDFPN solvers. Comparisons between using AlphaZero networks and PCN as heuristics show that PCN can solve more problems.
Wizard of Wikipedia: Knowledge-Powered Conversational Agents
Authors: Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, Jason Weston

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear  grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia.  We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.
Mapping the hyponymy relation of wordnet onto vector Spaces
Authors: Jean-Philippe Bernardy, Aleksandre Maskharashvili

In this paper, we investigate mapping the hyponymy relation of
 wordnet to feature vectors.
  We aim to model lexical knowledge in such a way that it can be used as
  input in generic machine-learning models, such as phrase entailment
  predictors.
  We propose two models. The first one leverages an existing mapping of
  words to feature vectors (fasttext), and attempts to classify
  such vectors as within or outside of each class. The second model is fully supervised,
  using solely wordnet as a ground truth. It maps each concept to an
  interval or a disjunction thereof.
  On the first model, we approach, but not quite attain state of the
  art performance. The second model can achieve near-perfect accuracy.