nils-herrmann/clean_scirepeval huggingface dataset:
Associative Conversation Model: Generating Visual Information from Textual Information
Authors: Yoichi Ishibashi, Hisashi Miyamori

In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector  fusing associative visual information and sentence textual information. A comparative experiment between our proposed model and a model without association showed that our proposed model is generating useful sentences by associating visual information related to sentences. Furthermore, analysis experiment of visual association showed that our proposed model generates (associates) visual information effective for sentence generation.
Transfer Learning for Bayesian HPO with End-to-End Meta-Features
Authors: Hadi Samer Jomaa, Sebastian Pineda Arango, Lars Schmidt-Thieme, Josif Grabocka

Hyperparameter optimization (HPO) is a crucial component of deploying machine learning models, however, it remains an open problem due to the resource-constrained number of possible hyperparameter evaluations. As a result, prior work focus on exploring the direction of transfer learning for tackling the sample inefficiency of HPO. In contrast to existing approaches, we propose a novel Deep Kernel Gaussian Process surrogate with Landmark Meta-features (DKLM) that can be jointly meta-trained on a set of source tasks and then transferred efficiently on a new (unseen) target task. We design DKLM to capture the similarity between hyperparameter configurations with an end-to-end meta-feature network that embeds the set of evaluated configurations and their respective performance. As a result, our novel DKLM can learn contextualized dataset-specific similarity representations for hyperparameter configurations. We experimentally validate the performance of DKLM in a wide range of HPO meta-datasets from OpenML and demonstrate the empirical superiority of our method against a series of state-of-the-art baselines.
Towards the Memorization Effect of Neural Networks in Adversarial Training
Authors: Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang

Recent studies suggest that "memorization" is one important factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly fitted DNNs can memorize the labels of many atypical samples, generalize their memorization to correctly classify test atypical samples and enjoy better test performance. While, DNNs which are optimized via adversarial training algorithms can also achieve perfect training performance by memorizing the labels of atypical samples, as well as the adversarially perturbed atypical samples. However, adversarially trained models always suffer from poor generalization, with both relatively low clean accuracy and robustness on the test set. In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples. Based on these two findings, we propose Benign Adversarial Training (BAT) which can facilitate adversarial training to avoid fitting "harmful" atypical samples and fit as more "benign" atypical samples as possible. In our experiments, we validate the effectiveness of BAT, and show it can achieve better clean accuracy vs. robustness trade-off than baseline methods, in benchmark datasets such as CIFAR100 and Tiny ImageNet.
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Authors: Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Remi Munos

The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning. In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third. We provide empirical evidence suggesting this is a serious issue in practice. Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance. We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences. We give empirical results on a number of domains comparing these three divergences. To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer
Authors: Ziqi Zhang, Cheng Deng, Kun Wei, Xu Yang

Semantic-level facial attribute transfer is a special task to edit facial attribute, when reference images are viewed as conditions to control the image editing. In order to achieve better performance, semantic-level facial attribute transfer needs to fulfil two requirements: (1) specific attributes extracted from reference face should be precisely transferred to target face; (2) irrelevant information should be completely retained after transferring. Some existing methods locate and modify local support regions of facial images, which are not effective when editing global attributes; the other methods disentangle the latent code as different attribute-relevant parts, which may transfer redundant knowledge to target faces. In this paper, we first propose a novel text-driven directional latent mapping network with semantic direction consistency (SDC) constrain to explore the latent semantic space for effective attribute editing, leveraging the semantic-aware knowledge of Contrastive Language-Image Pre-training (CLIP) model as guidance. This latent space manipulation strategy is designed to disentangle the facial attribute, removing the redundant knowledge in the transfer process. And on this basis, a novel attribute transfer method, named semantic directional decomposition network (SDD-Net), is proposed to achieve semantic-level facial attribute transfer by latent semantic direction decomposition, improving the interpretability and editability of our method. Extensive experiments on CelebA-HQ dataset show that our method achieves impressive performance over the state-of-the-art methods.
Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness
Authors: Haichao Zhang, Wei Xu

We propose a simple approach for adversarial training. The proposed approach utilizes an adversarial interpolation scheme for generating adversarial images and accompanying adversarial labels, which are then used in place of the original data for model training. The proposed approach is intuitive to understand, simple to implement and achieves state-of-the-art performance. We evaluate the proposed approach on a number of datasets including CIFAR10, CIFAR100 and SVHN. Extensive empirical results compared with several state-of-the-art methods against different attacks verify the effectiveness of the proposed approach.
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
Authors: Jinyi Liu, Zhi Wang, YAN ZHENG, Jianye HAO, Junjie Ye, Chenjia Bai, Pengyi Li

Many exploration strategies are built upon the optimism in the face of the uncertainty (OFU) principle for reinforcement learning. However, without considering the aleatoric uncertainty, existing methods may over-explore the state-action pairs with large randomness and hence are non-robust. In this paper, we explicitly capture the aleatoric uncertainty from a distributional perspective and propose an information-theoretic exploration method named Optimistic Value Distribution Explorer (OVD-Explorer). OVD-Explorer follows the OFU principle, but more importantly, it avoids exploring the areas with high aleatoric uncertainty through maximizing the mutual information between policy and the upper bounds of policy's returns. Furthermore, to make OVD-Explorer tractable for continuous RL, we derive a closed form solution, and integrate it with SAC, which, to our knowledge, for the first time alleviates the negative impact on exploration caused by aleatoric uncertainty for continuous RL. Empirical evaluations on the commonly used Mujoco benchmark and a novel GridChaos task demonstrate that OVD-Explorer can alleviate over-exploration and outperform state-of-the-art methods.
Improving zero-shot generalization in offline reinforcement learning using generalized similarity functions
Authors: Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson

Reinforcement learning (RL) agents are widely used for solving complex sequential decision making tasks, but still exhibit difficulty in generalizing to scenarios not seen during training. While prior online approaches demonstrated that using additional signals beyond the reward function can lead to better generalization capabilities in RL agents, i.e. using self-supervised learning (SSL), they struggle in the offline RL setting, i.e. learning from a static dataset. We show that the performance of online algorithms for generalization in RL can be hindered in the offline setting due to poor estimation of similarity between observations. We propose a new theoretically-motivated framework called Generalized Similarity Functions (GSF), which uses contrastive learning to train an offline RL agent to aggregate observations based on the similarity of their expected future behavior, where we quantify this similarity using generalized value functions. We show that GSF is general enough to recover existing SSL objectives while also improving zero-shot generalization performance on a complex offline RL benchmark, offline Procgen.
SSW-GAN: Scalable Stage-wise Training of Video GANs
Authors: Lluis Castrejon, Nicolas Ballas, Aaron Courville

Current state-of-the-art generative models for videos have high computational requirements that impede high resolution generations beyond a few frames. In this work we propose a stage-wise strategy to train Generative Adversarial Networks (GANs) for videos.  We decompose the generative process to first produce a downsampled video that is then spatially upscaled and temporally interpolated by subsequent stages. Upsampling stages are applied locally on temporal chunks of previous outputs to manage the computational complexity. Stages are defined as Generative Adversarial Networks, which are trained sequentially and independently. We validate our approach on Kinetics-600 and BDD100K, for which we train a three stage model capable of generating 128x128 videos with 100 frames.
Learning Contextual Perturbation Budgets for Training Robust Neural Networks
Authors: Jing Xu, Zhouxing Shi, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Liwei Wang

Existing methods for training robust neural networks generally aim to make models uniformly robust on all input dimensions. However, different input dimensions are not uniformly important to the prediction. In this paper, we propose a novel framework to train certifiably robust models and learn non-uniform perturbation budgets on different input dimensions, in contrast to using the popular $\ell_\infty$ threat model. We incorporate a perturbation budget generator into the existing certified defense framework, and perform certified training with generated perturbation budgets. In comparison to the radius of $\ell_\infty$ ball in previous works, the robustness intensity is measured by robustness volume which is the multiplication of perturbation budgets on all input dimensions. We evaluate our method on MNIST and CIFAR-10 datasets and show that we can achieve lower clean and certified errors on relatively larger robustness volumes, compared to methods using uniform perturbation budgets. Further with two synthetic datasets constructed from MNIST and CIFAR-10, we also demonstrate that the perturbation budget generator can produce semantically-meaningful budgets, which implies that the generator can capture contextual information and the sensitivity of different features in input images.
Reparameterized Variational Divergence Minimization for Stable Imitation
Authors: Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Eric Horvitz, Bill Dolan

State-of-the-art results in imitation learning are currently held by adversarial methods that iteratively estimate the divergence between student and expert policies and then minimize this divergence to bring the imitation policy closer to expert behavior. Analogous techniques for imitation learning from observations alone (without expert action labels), however, have not enjoyed the same ubiquitous successes. 
Recent work in adversarial methods for generative models has shown that the measure used to judge the discrepancy between real and synthetic samples is an algorithmic design choice, and that different choices can result in significant differences in model performance. Choices including Wasserstein distance and various $f$-divergences have already been explored in the adversarial networks literature, while more recently the latter class has been investigated for imitation learning. Unfortunately, we find that in practice this existing imitation-learning framework for using $f$-divergences suffers from numerical instabilities stemming from the combination of function approximation and policy-gradient reinforcement learning. In this work, we alleviate these challenges and offer a reparameterization of adversarial imitation learning as $f$-divergence minimization before further extending the framework to handle the problem of imitation from observations only. Empirically, we demonstrate that our design choices for coupling imitation learning and $f$-divergences are critical to recovering successful imitation policies. Moreover, we find that with the appropriate choice of $f$-divergence, we can obtain imitation-from-observation algorithms that outperform baseline approaches and more closely match expert performance in continous-control tasks with low-dimensional observation spaces. With high-dimensional observations, we still observe a significant gap with and without action labels, offering an interesting avenue for future work.
Unsupervised Neural Multi-Document Abstractive Summarization of Reviews
Authors: Eric Chu, Peter J. Liu

Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder trained so that the mean of the representations of the input reviews decodes to a reasonable summary-review.  We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components.  We show through metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews.
Approximation and non-parametric estimation of ResNet-type convolutional neural networks via block-sparse fully-connected neural networks
Authors: Kenta Oono, Taiji Suzuki

We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, filter size, and width are fixed. It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully-connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed. Our result is general in the sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. Thanks to the general theory, it is shown that learning on CNNs satisfies optimality in approximation and estimation of several important function classes.

As applications, we consider two types of function classes to be estimated: the Barron class and H\"older class. We prove the clipped empirical risk minimization (ERM) estimator can achieve the same rate as FNNs even the channel size, filter size, and width of CNNs are constant with respect to the sample size. This is minimax optimal (up to logarithmic factors) for the H\"older class. Our proof is based on sophisticated evaluations of the covering number of CNNs and the non-trivial parameter rescaling technique to control the Lipschitz constant of CNNs to be constructed.
Disentangled Representation Learning with Sequential Residual Variational Autoencoder
Authors: Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren

Recent advancements in unsupervised disentangled representation learning focus on extending the variational autoencoder (VAE) with an augmented objective function to balance the trade-off between disentanglement and reconstruction. We propose Sequential Residual Variational Autoencoder (SR-VAE) that defines a "Residual learning" mechanism as the training regime instead of the augmented objective function. Our proposed solution deploys two important ideas in a single framework: (1) learning from the residual between the input data and the accumulated reconstruction of sequentially added latent variables; (2) decomposing the reconstruction into decoder output and a residual term. This formulation encourages the disentanglement in the latent space by inducing explicit dependency structure, and reduces the bottleneck of VAE by adding the residual term to facilitate reconstruction. More importantly, SR-VAE eliminates the hyperparameter tuning, a crucial step for the prior state-of-the-art  performance using the objective function augmentation approach.  We demonstrate both qualitatively and quantitatively that SR-VAE improves the state-of-the-art  unsupervised disentangled representation learning on a variety of complex datasets.
Explicit Recall for Efficient Exploration
Authors: Honghua Dong, Jiayuan Mao, Xinyue Cui, Lihong Li

In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning.  This memory records structured trajectories that have led to interesting states in the past, and can be used by the agent to revisit those states more effectively.  In high-dimensional decision making problems, where deep reinforcement learning is considered crucial, our approach provides a simple, transparent and effective way that can be naturally combined with complex, deep learning models.  We show how such explicit memory may be used to enhance existing exploration algorithms such as intrinsically motivated ones and count-based ones, and demonstrate our method's advantages in various simulated environments.
Multi-Step Decentralized Domain Adaptation
Authors: Akhil Mathur, Shaoduo Gan, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane

Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios. In this paper, we highlight two significant bottlenecks for uDA, namely excessive centralization and poor support for distributed domain datasets. Our proposed framework, MDDA, is powered by a novel collaborator selection algorithm and an effective distributed adversarial training method, and allows for uDA methods to work in a decentralized and privacy-preserving way.
Self-Supervised Variational Auto-Encoders
Authors: Ioannis Gatopoulos, Jakub Mikolaj Tomczak

Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised Variational Auto-Encoder (selfVAE), that utilizes deterministic and discrete transformations of data. This class of models allows performing both conditional and unconditional sampling while simplifying the objective function. First, we use a single self-supervised transformation as a latent variable, where a transformation is either downscaling or edge detection. Next, we consider a hierarchical architecture, i.e., multiple transformations, and we show its benefits compared to the VAE. The flexibility of selfVAE in data reconstruction finds a particularly interesting use case in data compression tasks, where we can trade-off memory for better data quality, and vice-versa. We present the performance of our approach on three benchmark image data (Cifar10, Imagenette64, and CelebA).
Simplicial Complex Networks
Authors: Mohammad Firouzi, Sadra Boreiri, Hamed Firouzi

Universal approximation property of neural networks is one of the motivations to use these models in various real-world problems. However, this property is not the only characteristic that makes neural networks unique as there is a wide range of other approaches with similar property. Another characteristic which makes these models interesting is that they can be trained with the backpropagation algorithm which allows an efficient gradient computation and gives these universal approximators the ability to efficiently learn complex manifolds from a large amount of data in different domains. Despite their abundant use in practice, neural networks are still not well understood and a broad range of ongoing research is to study the interpretability of neural networks. On the other hand, topological data analysis (TDA) relies on strong theoretical framework of (algebraic) topology along with other mathematical tools for analyzing possibly complex datasets. In this work, we leverage a universal approximation theorem originating from algebraic topology to build a connection between TDA and common neural network training framework. We introduce the notion of automatic subdivisioning and devise a particular type of neural networks for regression tasks: Simplicial Complex Networks (SCNs). SCN's architecture is defined with a set of bias functions along with a particular policy during the forward pass which alternates the common architecture search framework in neural networks. We believe the view of SCNs can be used as a step towards building interpretable deep learning models. Finally, we verify its performance on a set of regression problems.