nils-herrmann/clean_scirepeval huggingface dataset:
Topic-Based Question Generation
Authors: Wenpeng Hu, Bing Liu, Rui Yan, Dongyan Zhao, Jinwen Ma

Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.
On the Inversion of Deep Generative Models
Authors: Aviad Aberdam, Dror Simon, Michael Elad

Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, we define conditions that rely only on the cardinalities of the hidden layer and are applicable to any inversion algorithm (gradient descent, deep encoder, etc.), under which such generative models are invertible with a unique solution. Importantly, the proposed analysis is applicable to any trained model, and does not depend on Gaussian i.i.d. weights. Furthermore, we introduce two layer-wise inversion pursuit algorithms for trained generative networks of arbitrary depth, where one of them is accompanied by recovery guarantees. Finally, we validate our theoretical results numerically and show that our method outperforms gradient descent when inverting such generators, both for clean and corrupted signals.
Image BERT Pre-training with Online Tokenizer
Authors: Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong

The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
In this work, we study masked image modeling (MIM) and indicate the necessity and challenges of using a semantically meaningful visual tokenizer.
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer. 
Specifically, we perform self-distillation on masked patch tokens and take the teacher network as the online tokenizer, along with self-distillation on the class token to acquire visual semantics.
The online tokenizer is jointly learnable with the MIM objective and dispenses with a multi-stage training pipeline where the tokenizer needs to be pre-trained beforehand.
We show the prominence of iBOT by achieving an 82.3% linear probing accuracy and an 87.8% fine-tuning accuracy evaluated on ImageNet-1K.
Beyond the state-of-the-art image classification results, we underline emerging local semantic patterns, which helps the models to obtain strong robustness against common corruptions and achieve leading results on dense downstream tasks, e.g., object detection, instance segmentation, and semantic segmentation.
Deep Reinforcement Learning For Wireless Scheduling with Multiclass Services
Authors: Apostolos Avranas, Marios Kountouris, Philippe Ciblat

In this paper, we investigate the problem of scheduling and resource allocation over a time varying set of clients with heterogeneous demands. This problem appears when service providers need to serve traffic generated by users with different classes of requirements. We thus have to allocate bandwidth resources over time to efficiently satisfy these demands within a limited time horizon. This is a highly intricate problem and solutions may involve tools stemming from diverse fields like combinatorics and optimization. Recent work has successfully proposed Deep Reinforcement Learning (DRL) solutions, although not yet for heterogeneous user traffic. We propose a deep deterministic policy gradient algorithm combining state of the art techniques, namely Distributional RL and Deep Sets, to train a model for heterogeneous traffic scheduling. We test on diverse number scenarios with different time dependence dynamics, users’ requirements, and resources available, demonstrating consistent results. We evaluate the algorithm on a wireless communication setting and show significant gains against state-of-the-art conventional algorithms from combinatorics and optimization (e.g. Knapsack, Integer Linear Programming, Frank-Wolfe).
Self-Supervised Representation Learning via Latent Graph Prediction
Authors: Yaochen Xie, Zhao Xu, Shuiwang Ji

Self-supervised learning (SSL) of graph neural networks is emerging as a promising way of leveraging unlabeled data. Currently, most methods are based on contrastive learning adapted from the image domain, which requires view generation and a sufficient number of negative samples. In contrast, existing predictive models do not require negative sampling, but lack theoretical guidance on the design of pretext training tasks. In this work, we propose the LaGraph, a theoretically grounded predictive SSL framework based on latent graph prediction. Learning objectives of LaGraph are derived as self-supervised upper bounds to objectives for predicting unobserved latent graphs. In addition to its improved performance, LaGraph provides explanations for recent successes of predictive models that include invariance-based objectives. We provide theoretical analysis comparing LaGraph to related methods in different domains. Our experimental results demonstrate the superiority of LaGraph in performance and the robustness to decreasing of training sample size on both graph-level and node-level tasks.
Contrastive Embeddings for Neural Architectures
Authors: Daniel Hesslow, Iacopo Poli

The performance of algorithms for neural architecture search strongly depends on the parametrization of the search space. We use contrastive learning to identify networks across different initializations based on their data Jacobians and their number of parameters, and automatically produce the first architecture embeddings independent from the parametrization of the search space. Using our contrastive embeddings, we show that traditional black-box optimization algorithms, without modification, can reach state-of-the-art performance in Neural Architecture Search. As our method provides a unified embedding space, we successfully perform transfer learning between search spaces. Finally, we show the evolution of embeddings during training, motivating future studies into using embeddings at different training stages to gain a deeper understanding of the networks in a search space.
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
Authors: Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee

We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars).  The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset of properties from each domain, enabling the generation of images that did not exist in any domain exclusively.  This challenging problem requires an accurate disentanglement of object shape, appearance, and background from each domain, so that the appearance and shape factors from the two domains can be interchanged. We augment an existing approach that can disentangle factors within a single domain but struggles to do so across domains.  Our key technical contribution is to represent object appearance with a differentiable histogram of visual features, and to optimize the generator so that two images with the same latent appearance factor but different latent shape factors produce similar histograms. On multiple multi-domain datasets, we demonstrate our method leads to accurate and consistent appearance and shape transfer across domains.
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Authors: Paul Barde, Julien Roy, Félix G. Harvey, Derek Nowrouzezahrai, Christopher Pal

A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team. In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection. We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm. To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment. We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method. Our experiments show that the proposed methods lead to significant improvements on cooperative problems. We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.
GRAPHS, ENTITIES, AND STEP MIXTURE
Authors: Kyuyong Shin, Wonyoung Shin, Jung-Woo Ha, Sunyoung Kwon

Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks. Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation. Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs. To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM). GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly. These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations. With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks. Furthermore, we empirically demonstrate the significance of considering global information. The source code will be publicly available in the near future.
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Authors: Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational cost (e.g., 100 times more than that of the project gradient descent attack) makes AA infeasible for practitioners with limited computational resources, and also hinders applications of AA in the adversarial training (AT). In this paper, we propose a novel method, minimum-margin (MM) attack, to fast and reliably evaluate adversarial robustness. Compared with AA, our method achieves comparable performance but only costs 3% of the computational time in extensive experiments. The reliability of our method lies in that we evaluate the quality of adversarial examples using the margin between two targets that can precisely identify the most adversarial example. The computational efficiency of our method lies in an effective Sequential TArget Ranking Selection (STARS) method, ensuring that the cost of the MM attack is independent of the number of classes. The MM attack opens a new way for evaluating adversarial robustness and contributes a feasible and reliable method to generate high-quality adversarial examples in AT.
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
Authors: Arda Sahiner, Tolga Ergen, Batu Ozturkler, Burak Bartan, John M. Pauly, Morteza Mardani, Mert Pilanci

Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural networks, posing a non-transparent optimization problem which is non-convex and non-concave over the generator and discriminator, respectively. Such networks are often heuristically optimized with gradient descent-ascent (GDA), but it is unclear whether the optimization problem contains any saddle points, or whether heuristic methods can find them in practice. In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games. Using this convex duality interpretation, we further demonstrate the impact of different activation functions of the discriminator. Our observations are verified with numerical results demonstrating the power of the convex interpretation, with an application in progressive training of convex architectures corresponding to linear generators and quadratic-activation discriminators for CelebA image generation. The code for our experiments is available at https://github.com/ardasahiner/ProCoGAN.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Authors: Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024^2 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder. With thirteen datasets covering a wide variety of image domains (The datasets and code are available at https://github.com/odegeasslbc/FastGAN-pytorch), we show our model's superior performance compared to the state-of-the-art StyleGAN2, when data and computing budget are limited.
Multi-View Disentangled Representation
Authors: Zongbo Han, Changqing Zhang, Huazhu Fu, Qinghua Hu, Joey Tianyi Zhou

Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition. Recently great efforts have focused on learning unified or latent representations to integrate information from different views for specific tasks. These approaches generally assume simple or implicit relationships between different views and as a result are not able to flexibly and explicitly depict the correlations among these views. To address this, we firstly propose the definition and conditions for multi-view disentanglement providing general instructions for disentangling representations between different views. Furthermore, a novel objective function is derived to explicitly disentangle the multi-view data into a shared part across different views and a (private) exclusive part within each view. Experiments on a variety of multi-modal datasets demonstrate that our objective can effectively disentangle information from different views while satisfying the disentangling conditions.
Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes
Authors: Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho, Yoshua Bengio

In this paper, we extend neural Turing machine (NTM) into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both continuous, differentiable and discrete, non-differentiable read/write mechanisms. We investigate the mechanisms and effects for learning to read and  write to a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU-controller. The D-NTM is evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM baselines. We also provide further experimental results on sequential MNIST, associative recall and copy tasks.
Manifold Distance Judge, an Adversarial Samples Defense Strategy Based on Service Orchestration
Authors: Mengxin Zhang, Xiaofeng QIU

Deep neural networks (DNNs) are playing an increasingly significant role in the modern world. However, they are weak to adversarial examples that are generated by adding specially crafted perturbations. Most defenses against adversarial examples focused on refining the DNN models, which often sacrifice the performance and computational cost of models on benign samples. In this paper, we propose a manifold distance detection method to distinguish between legitimate samples and adversarial samples by measuring the different distances on the manifold. The manifold distance detection method neither modifies the protected models nor requires knowledge of the process for generating adversarial samples. Inspired by the effectiveness of the manifold distance detection, we demonstrated a well-designed orchestrated defense strategy, named Manifold Distance Judge (MDJ), which selects the best image processing method that will effectively expand the manifold distance between legitimate and adversarial samples, and thus, enhances the performance of the following manifold distance detection method. Tests on the ImageNet dataset, the MDJ is effective against the most adversarial samples under whitebox, graybox, and blackbox attack scenarios. We show empirically that the orchestration strategy MDJ is significantly better than Feature Squeezing on the recall rate. Meanwhile, MDJ achieves high detection rates against CW attack and DI-FGSM attack.
LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ENTROPY CONSTRAINED OPTIMAL TRANSPORT
Authors: Huan Liu, George Zhang, Jun Chen, Ashish J Khisti

We study an extension of lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing. We formulate this as a generalization of optimal transport with an entropy bottleneck to account for the rate constraint due to compression. We provide expressions for the tradeoff between  compression rate and the achievable distortion with and without shared common randomness between the encoder and decoder.  We study the examples of binary, uniform and Gaussian sources (in an asymptotic setting) in detail and  demonstrate that shared randomness can strictly improve the tradeoff. For the case without common randomness and squared-Euclidean distortion, we show that the optimal solution partially decouples into the problem of optimal compression and transport and also characterize the penalty associated with fully decoupling them. We provide experimental results by training deep learning end-to-end compression systems for performing denoising on SVHN and super-resolution on MNIST suggesting consistency with our theoretical results.
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search
Authors: Jakob J. Hollenstein, Erwan Renaudo, Justus Piater

Most Deep Reinforcement Learning methods perform local search and
therefore are prone to get stuck on non-optimal
solutions. Furthermore, in simulation based training, such as
domain-randomized simulation training, the availability of a simulation
model is not exploited, which potentially decreases
efficiency. To overcome issues of local search and exploit
access to simulation models, we propose the use of kino-dynamic
planning methods as part of a model-based reinforcement learning
method and to learn in an off-policy fashion from solved planning
instances. We show that, even on a simple toy domain, D-RL
methods (DDPG, PPO, SAC) are not immune to local optima and
require additional exploration mechanisms. We show that our
planning method exhibits a better state space coverage, collects
data that allows for better policies than D-RL methods without
additional exploration mechanisms and that starting from the
planner data and performing additional training results in as
good as or better policies than vanilla D-RL methods, while also
creating data that is more fit for re-use in modified tasks.
Emergent Complexity via Multi-Agent Competition
Authors: Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch

Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment.  This suggests that a highly capable agent requires a complex environment for training.  In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself.  We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty.
This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX
Impact of the latent space on the ability of GANs to fit the distribution
Authors: Thomas Pinetz, Daniel Soukup, Thomas Pock

The goal of generative models is to model the underlying data distribution of a
sample based dataset. Our intuition is that an accurate model should in principle
also include the sample based dataset as part of its induced probability distribution.
To investigate this, we look at fully trained generative models using the Generative
Adversarial Networks (GAN) framework and analyze the resulting generator
on its ability to memorize the dataset. Further, we show that the size of the initial
latent space is paramount to allow for an accurate reconstruction of the training
data. This gives us a link to compression theory, where Autoencoders (AE) are
used to lower bound the reconstruction capabilities of our generative model. Here,
we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
(2018)). Given a small latent space, the AE produces low quality and the GAN
produces high quality outputs from a perceptual viewpoint. In contrast, the distortion
error is smaller for the AE. By increasing the dimensionality of the latent
space the distortion decreases for both models, but the perceptual quality only
increases for the AE.