nils-herrmann/clean_scirepeval huggingface dataset:
UCB EXPLORATION VIA Q-ENSEMBLES
Authors: Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments show significant gains on the Atari benchmark.
SPLID: Self-Imitation Policy Learning through Iterative Distillation
Authors: Zhihan Liu, Hao Sun, Bolei Zhou

Goal-Conditioned continuous control tasks remain challenging due to the sparse reward signals. To address this issue, many relabelling methods like Hindsight Experience Replay have been developed and bring significant improvement. Though relabelling methods provide an alternative to an expert demonstration, the majority of the relabelled data are not optimal. If we can improve the quality of the relabelled data, the sample efficiency, as well as the agent performance, should be improved. To this end, we propose a novel meta-algorithm Self-Imitation Policy Learning through Iterative Distillation (SPLID)  which relies on the concept of $\delta$-distilled policy to iteratively level up the quality of the target data and agent mimics from the relabeled target data. Under certain assumptions, we show that SPLID has good theoretical properties of performance improvement and local convergence guarantee. Specifically, in the deterministic environment, we develop a practical implementation of SPLID, which imposes $\delta$-distilled policy by discriminating First Hit Time (FHT). Experiments show that SPLID outperforms previous Goal-Conditioned RL methods with a substantial margin.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
Authors: Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail. Counterfactual methods, on the other hand, indicate how to perturb the input to change the model prediction, providing details about the model's decision-making. Unfortunately, current counterfactual methods make ambiguous interpretations as they combine multiple biases of the model and the data in a single counterfactual interpretation of the model's decision. Moreover, these methods tend to generate trivial counterfactuals about the model's decision, as they often suggest to exaggerate or remove the presence of the attribute being classified. Trivial counterfactuals are usually not valuable, since the information they provide is often already known to the system's designer. In this work, we propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. We will make the code public.
Manifold Alignment via Feature Correspondence
Authors: Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy

We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.
Geometry aware convolutional filters for omnidirectional images representation
Authors: Renata Khasanova, Pascal Frossard

Due to their wide field of view, omnidirectional cameras are frequently used by autonomous vehicles, drones and robots for navigation and other computer vision tasks. The images captured by such cameras, are often analysed and classified with techniques designed for planar images that unfortunately fail to properly handle the native geometry of such images. That results in suboptimal performance, and lack of truly meaningful visual features. In this paper we aim at improving popular deep convolutional neural networks so that they can properly take into account the specific properties of omnidirectional data. In particular we propose an algorithm that adapts convolutional layers, which often serve as a core building block of a CNN, to the properties of omnidirectional images. Thus, our filters have a shape and size that adapts with the location on the omnidirectional image. We show that our method is not limited to spherical surfaces and is able to incorporate the knowledge about any kind of omnidirectional  geometry inside the deep learning network. As depicted by our experiments, our method outperforms the existing deep neural network techniques for omnidirectional image classification and compression tasks.
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Authors: Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, there are issues that remain to be tackled in order to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters  for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.
An analytic theory of generalization dynamics and transfer learning in deep linear networks
Authors: Andrew K. Lampinen, Surya Ganguli

Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can improve generalization on individual tasks. However we lack analytic theories that can quantitatively predict how the degree of knowledge transfer depends on the relationship between the tasks. We develop an analytic theory of the nonlinear dynamics of generalization in deep linear networks, both within and across tasks. In particular, our theory provides analytic solutions to the training and testing error of deep networks as a function of training time, number of examples, network size and initialization, and the task structure and SNR. Our theory reveals that deep networks progressively learn the most important task structure first, so that generalization error at the early stopping time primarily depends on task structure and is independent of network size. This suggests any tight bound on generalization error must take into account task structure, and explains observations about real data being learned faster than random data. Intriguingly our theory also reveals the existence of a learning algorithm that proveably out-performs neural network training through gradient descent. Finally, for transfer learning, our theory reveals that knowledge transfer depends sensitively, but computably, on the SNRs and input feature alignments of pairs of tasks.
LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
Authors: Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach significantly outperforms the state-of-the-art in open-set domain adaptation.
Locality and Compositionality in Zero-Shot Learning
Authors: Tristan Sylvain, Linda Petrini, Devon Hjelm

In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). 
In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed.
The results of our experiment show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.
Autostacker: an Automatic Evolutionary Hierarchical  Machine Learning System
Authors: Boyuan Chen, Warren Mo, Ishanu Chattopadhyay, Hod Lipson

This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm. Neither prior domain knowledge about the data nor feature preprocessing is needed. We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC). By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time. These pipelines can be used as is or as a starting point for human experts to build on. By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures. As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.
Wasserstein diffusion on graphs with missing attributes
Authors: Zhixian Chen, Tengfei Ma, Yangqiu Song, Yang Wang

Many real-world graphs are attributed graphs where nodes are associated with non-topological features. While attributes can be missing anywhere in an attributed graph, most of existing node representation learning approaches do not consider such incomplete information.
In this paper, we propose a general non-parametric framework to mitigate this problem. Starting from a decomposition of the attribute matrix, we transform node features into discrete distributions in a lower-dimensional space equipped with the Wasserstein metric. On this Wasserstein space, we propose Wasserstein graph diffusion to smooth the distributional representations of nodes with information from their local neighborhoods. This allows us to reduce the distortion caused by missing attributes and obtain integrated representations expressing information of both topology structures and attributes. We then pull the nodes back to the original space and produce corresponding point representations to facilitate various downstream tasks. To show the power of our representation method, we designed two algorithms based on it for node classification (with missing attributes) and matrix completion respectively, and demonstrate their effectiveness in experiments.
AestheticNet: Reducing bias in facial data sets under ethical considerations
Authors: Michael Danner, Muhammad Awais Tanvir Rana, Thomas Weber, Tobias Gerlach, Patrik Huber, Matthias Rätsch, Josef Kittler

Facial Beauty Prediction (FBP) aims to develop a machine that can automatically evaluate facial attractiveness. Usually, these results were highly correlated with human ratings, and therefore also reflected human bias in annotations. Everyone will have biases that are usually subconscious and not easy to notice. Unconscious bias deserves more attention than explicit discrimination. It affects moral judgement and can evade moral responsibility, and we cannot eliminate it completely. A new challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. Our experiments prove that human aesthetic judgements are usually biased. In this work, we introduce AestheticNet, the most advanced attractiveness prediction network, with a Pearson correlation coefficient of 0.9601, which is significantly better than the competition. This network is then used to enrich the training data with synthetic images in order to overwrite the ground truth values with fair assessments.
We propose a new method to generate an unbiased CNN to improve the fairness of machine learning. Prediction and recommender systems based on Artificial Intelligence (AI) technology are widely used in various sectors of industry, such as intelligent recruitment, security, etc. Therefore, their fairness is very important. Our research provides a practical example of how to build a fair and trustable AI.
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Authors: Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire

We prove a multiclass boosting theory for the ResNet architectures which simultaneously creates a new technique for multiclass boosting and provides a new algorithm for ResNet-style architectures.  Our proposed training algorithm, BoostResNet, is particularly suitable in non-differentiable architectures.  Our method only requires the relatively inexpensive sequential training of T "shallow ResNets". We prove that the training error decays exponentially with the depth T if the weak module classifiers that we train perform slightly better than some weak baseline.  In other words, we propose a weak learning condition and prove a boosting theory for ResNet under the weak learning condition.  A generalization error bound based on margin theory is proved and suggests that ResNet could be resistant to overfitting using a network with l_1 norm bounded weights.
Long Document Summarization with Top-Down and Bottom-Up Representation Inference
Authors: Bo Pang, Erik Nijkamp, Wojciech Maciej Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Text summarization aims to condense long documents and retain key information. Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents. Most recent models infer the latent representations with a transformer encoder, which is purely bottom-up. Also, self-attention-based inference models face the challenge of quadratic complexity with respect to sequence length. We propose a principled inference framework to improve summarization models on these two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency at a coarser time scale and the bottom token level preserves the details. Critically, this hierarchical structure enables token representations to be updated in both a bottom-up and top-down manner. In the bottom-up pass, token representations are inferred with local self-attention to leverage its efficiency. Top-down correction is then applied to allow tokens to capture long-range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets, including narrative, conversational, scientific documents and news. Our model achieves (1) competitive or better performance on short documents with higher memory and compute efficiency, compared to full attention transformers, and (2) state-of--the-art performance on a wide range of long document summarization benchmarks, compared to recent efficient transformers. We also show that our model can summarize an entire book and achieve competitive performance using $0.27\%$ parameters (464M vs. 175B) and much less training data, compared to a recent GPT-3-based model. These results indicate the general applicability and benefits of the proposed framework.
Quadtree Attention for Vision Transformers
Authors: Shitao Tang, Jiahui Zhang, Siyu Zhu, Ping Tan

Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc. We introduce QuadTree Attention, which reduces the computational complexity from quadratic to linear. Our quadtree transformer builds token pyramids and computes attention in a coarse-to-fine manner. At each level, the top K patches with the highest attention scores are selected, such that at the next level, attention is only evaluated within the relevant regions corresponding to these top K patches. We demonstrate that quadtree attention achieves state-of-the-art performance in various vision tasks, e.g. with 4.0% improvement in feature matching on ScanNet, about 50% flops reduction in stereo matching, 0.4-1.5% improvement in top-1 accuracy on ImageNet classification, 1.2-1.8% improvement on COCO object detection, and 0.7-2.4% improvement on semantic segmentation over previous state-of-the-art transformers. The codes are available at https://github.com/Tangshitao/QuadtreeAttention.
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression
Authors: Wenyuan Zeng, Raquel Urtasun

Model compression can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and accuracy, popular compression techniques usually rely on hand-crafted heuristics and
require manually setting the compression ratio of each layer. This process is typically costly and suboptimal. In this paper, we propose a Multi-Layer Pruning method (MLPrune), which is theoretically sound, and can automatically decide appropriate compression ratios for all layers. Towards this goal, we use an efficient approximation of the Hessian as our pruning criterion, based on a Kronecker-factored Approximate Curvature method. We demonstrate the effectiveness of our method on several datasets and architectures, outperforming previous state-of-the-art by a large margin. Our experiments show that we can compress AlexNet and VGG16 by 25x without loss in accuracy on ImageNet. Furthermore, our method has much fewer hyper-parameters and requires no expert knowledge.
XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings
Authors: Amelie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy

Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions.  We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.