pwc-archive/papers-with-abstracts huggingface dataset:
R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations
Authors: Shengxin Qian
Date: 2021-01-01 00:00:00
Tasks: Foreground Segmentation, Object, Representation Learning, Segmentation

Decomposing a complex scene into multiple objects is a natural instinct of an intelligent vision system. Recently, the interest in unsupervised scene representation learning emerged and many previous works tackle this by decomposing scene into object representations either in the form of segmentation masks or position and scale latent variables (i.e. bounding boxes). We observe that these two types of representation both contain object geometric information and should be consistent with each other. Inspired by this observation, we provide an unsupervised generative framework called R-MONet that can generate objects geometric representation in the form of bounding boxes and foreground segmentation masks simultaneously. While bounding boxes can represent the region of interest for generating foreground segmentation masks, the foreground segmentation masks can also be used to supervise bounding boxes learning with the Multi-Otsu Thresholding method. Through the experiments on CLEVR and Multi-dSprites datasets, we show that ensuring the consistency of two types of representation can help the model to decompose the scene and learn better object geometric representations.
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
Authors: Charan Reddy, Soroush Mehri, Deepak Sharma, Samira Shabanian, Sina Honari
Date: 2021-06-08 00:00:00
Tasks: Age And Gender Classification, Benchmarking, Bias Detection, Fairness, Gender Bias Detection, Image Classification, imbalanced classification, Representation Learning

With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning. In this paper, we evaluate different fairness methods trained with deep neural networks on a common synthetic dataset and a real-world dataset to obtain a better insight into the working of these methods. In particular, we train about 3000 different models in various setups, including imbalanced and correlated data configurations, to verify the limits of the current models and better understand in which setups they are subject to failure. Our results show that the bias of models increase as datasets become more imbalanced or datasets attributes become more correlated, the level of dominance of correlated sensitive dataset features impact bias, and the sensitive information remains in the latent representation even when bias-mitigation algorithms are applied. Overall, we present a dataset, propose various challenging evaluation setups, and rigorously evaluate recent promising bias-mitigation algorithms in a common framework and publicly release this benchmark, hoping the research community would take it as a common entry point for fair deep learning.
Pre-training Text-to-Text Transformers to Write and Reason with Concepts
Authors: Wangchunshu Zhou, Dong-Ho Lee, Ravi Kiran Selvam, Seyeon Lee, Xiang Ren
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Common Sense Reasoning, Language Modelling, Natural Language Understanding, Transfer Learning

Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding of the text. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational and compositional commonsense knowledge about everyday concepts, which is crucial to many downstream tasks requiring commonsense reasoning. To augment PTLMs with common sense, we propose generative and contrastive objectives as intermediate self-supervised pre-training tasks between general pre-training and downstream task-specific fine-tuning. We also propose a joint training framework to unify generative and contrastive objectives so that these objectives can be more effective.
Our proposed objectives can pack more commonsense knowledge into the parameters of a pre-trained text-to-text transformer without relying on external knowledge bases, yielding better performance on both NLU and NLG tasks. We apply our method on a pre-trained T5 model in an intermediate task transfer learning fashion to train a concept-aware language model (CALM) and experiment with five commonsense benchmarks (four NLU tasks and one NLG task). Experimental results show that CALM outperforms baseline methods by a consistent margin.
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty
Authors: Haichuan Gao, Zhile Yang, Tian Tan, Feng Chen
Date: 2021-01-01 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current reinforcement learning generally uses discounted return as its learning objective. However, real-world tasks may often demand a high success rate, which can be quite different from optimizing rewards. In this paper, we explicitly formulate the success rate as an undiscounted form of return with {0, 1}-binary reward function. Unfortunately, applying traditional Bellman updates to value function learning can be problematic for learning undiscounted return, and thus not suitable for optimizing success rate. From our theoretical analysis, we discover that values across different states tend to converge to the same value, resulting in the agent wandering around those states without making any actual progress. This further leads to reduced learning efficiency and inability to complete a task in time. To combat the aforementioned issue, we propose a new method, which introduces Loop Penalty (LP) into value function learning, to penalize disoriented cycling behaviors in the agent's decision-making. We demonstrate the effectiveness of our proposed LP on three environments, including grid-world cliff-walking, Doom first-person navigation and robot arm control, and compare our method with Q-learning, Monte-Carlo and Proximal Policy Optimization (PPO). Empirically, LP improves the convergence of training and achieves a higher success rate.
Data Transformer for Anomalous Trajectory Detection
Authors: Anonymous
Date: 2021-01-01 00:00:00
Tasks: Anomaly Detection

Anomaly detection is an important task in many traffic applications. Methods based on convolutional neural networks reach state-of-the-art accuracy; however, they typically rely on supervised training with large labeled data and the trained network is only applicable to the intersection that the training data are collected from. Considering that anomaly data are generally hard to obtain, we present data transformation methods for converting data obtained from one intersection to other intersections to mitigate the effort of training data collection. We demonstrate our methods on the task of anomalous trajectory detection and leverage an unsupervised method that require only normal trajectories for network training. We proposed a general model and a universal model for our transformation methods. The general model focuses on saving data collection effort; while the universal model aims at training a universal network for being used by other intersections. We evaluated our methods on the dataset with trajectories collected from GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, our methods still can achieve state-of-the-art accuracy for anomalous trajectory detection.
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning
Authors: Ran Tao, Marios Savvides
Date: 2021-01-01 00:00:00
Tasks: Few-Shot Learning

In this work, we focus on improving the novel class generalization of few-shot learning. By addressing the difference between feature distributions of base and novel classes, we propose the adaptive feature distribution method which is to finetune one scale vector using the support set of novel classes. The scale vector is applied on the normalized feature distribution and by using one scale vector to reshape the feature space manifold, we obtain consistent performance improvement for both in-domain and cross-domain evaluations. By simply finetuning one scale vector using 5 images, we observe a $2.23\%$ performance boost on 5-way 1-shot cross-domain evaluation with CUB over statistics results of 2000 episodes. This approach is simple yet effective. By just finetuning a single scale vector we provide a solution of reducing number of parameters while still obtain generalization ability for few-shot learning. We achieve the state-of-the-art performance on mini-Imagenet, tiered-Imagenet as well as cross-domain evaluation on CUB.
Graph Learning via Spectral Densification
Authors: Zhuo Feng, Yongyu Wang, Zhiqiang Zhao
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dimensionality Reduction, Graph Learning

Graph learning plays important role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, data clustering, and visualization, etc. For the first time, we present a highly-scalable spectral graph densification approach (GRASPEL) for graph learning from data. By limiting the precision matrix to be a graph-Laplacian-like matrix in graphical Lasso, our approach aims to learn ultra-sparse undirected graphs from potentially high-dimensional input data. A very unique property of the graphs learned by GRASPEL is that the spectral embedding (or approximate effective-resistance) distances on the graph will encode the similarities between the original input data points. By interleaving the latest high-performance nearly-linear
time spectral methods, ultrasparse yet spectrally-robust graphs can be learned by identifying and including the most spectrally-critical edges into the graph. Compared with prior state-of-the-art graph learning approaches, GRASPEL is more scalable and allows substantially improving computing efficiency and solution quality of a variety of data mining and
machine learning applications, such as manifold learning, spectral clustering (SC), and dimensionality reduction.
Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions
Authors: Yun Kuen Cheung, Yixin Tao
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1

It is of primary interest for AI/ML to understand how agents learn and interact dynamically in competitive environments and games (e.g. GANs). But over the past few decades, this has been shown to be a difficult task, as irregular behaviors are commonly observed in such systems. This can be explained theoretically, for instance, by the works of Cheung and Piliouras (COLT 2019, NeurIPS 2020), which showed that in two-person zero-sum games, if agents employ one of the most well-known learning algorithms, Multiplicative Weights Update (MWU), then Lyapunov chaos occurs everywhere in the payoff space. Understanding chaos is an important but long-ignored aspect of AI/ML, as chaos captures the sensitivity to round-off errors, which exist in every step of learning processes. Thus, chaos can severely affect predictability and reproducibility of learning systems.

We study how persistent chaos can occur in the more general normal game settings, where the agents might have motivation to coordinate (which is not true for zero-sum games) and the number of agents can be arbitrary.

We characterize bimatrix games where MWU, its optimistic variant (OMWU) or Follow-the-Regularized-Leader (FTRL) algorithms is Lyapunov chaotic almost everywhere in the payoff space. Technically, our characterization is derived by extending the volume-expansion argument of Cheung and Piliouras via the canonical game decomposition into zero-sum and coordination components. Interestingly, the two components induce opposite volume-changing behaviors, so the overall behavior can be analysed by comparing the strengths of the components against each other. The comparison is done via our new notion of "matrix domination" or via a linear program. For multi-player games, we present a local equivalence of volume change between general games and graphical games, which is used to perform volume and chaos analyses of MWU and OMWU in potential games.
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization
Authors: Nitin Rathi, Kaushik Roy
Date: 2021-01-01 00:00:00
Tasks: Computational Efficiency, image-classification, Image Classification

Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. The state-of-the-art SNNs suffer from high inference latency, resulting from inefficient input encoding, and sub-optimal settings of the neuron parameters (firing threshold, and membrane leak). We propose DIET-SNN, a low latency deep spiking network that is trained with gradient descent to optimize the membrane leak and the firing threshold along with other network parameters (weights). The membrane leak and threshold for each layer of the SNN are optimized with end-to-end backpropagation to achieve competitive accuracy at reduced latency. The analog pixel values of an image are directly applied to the input layer of DIET-SNN without the need to convert to spike-train. The first convolutional layer is trained to convert inputs into spikes where leaky-integrate-and-fire (LIF) neurons integrate the weighted inputs and generate an output spike when the membrane potential crosses the trained firing threshold. The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and linear layers of the network. The reduced latency combined with high activation sparsity provides large improvements in computational efficiency. We evaluate DIET-SNN on image classification tasks from CIFAR and ImageNet datasets on VGG and ResNet architectures. We achieve top-1 accuracy of 69% with 25 timesteps (inference latency) on the ImageNet dataset with 3.1x less compute energy than an equivalent standard ANN. Additionally, DIET-SNN performs 5-100x faster inference compared to other state-of-the-art SNN models.
Provably robust classification of adversarial examples with detection
Authors: Fatemeh Sheikholeslami, Ali Lotfi, J Zico Kolter
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Classification, General Classification, Robust classification

Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations.  Although it may intuitively seem easier to simply detect attacks rather than build a robust classifier, this has not bourne out in practice even empirically, as most detection methods have subsequently been broken by adaptive attacks, thus necessitating \emph{verifiable} performance for detection mechanisms.  In this paper, we propose a new method for jointly training a provably robust classifier and detector.  Specifically, we show that by introducing an additional "abstain/detection" into a classifier, we can modify existing certified defense mechanisms to allow the classifier to either robustly classify \emph{or} detect adversarial attacks.  We extend the common interval bound propagation (IBP) method for certified robustness under $\ell_\infty$ perturbations to account for our new robust objective, and show that the method outperforms traditional IBP used in isolation, especially for large perturbation sizes.  Specifically, tests on MNIST and CIFAR-10 datasets exhibit promising results, for example with provable robust error less than $63.63\%$ and $67.92\%$, for $55.6\%$ and $66.37\%$ natural error, for $\epsilon=8/255$ and $16/255$ on the CIFAR-10 dataset, respectively.
Deep Ensemble Kernel Learning
Authors: Devanshu Agrawal, Jacob D Hinkle
Date: 2021-01-01 00:00:00
Tasks: Gaussian Processes

Gaussian processes (GPs) are nonparametric Bayesian models that are both flexible and robust to overfitting. One of the main challenges of GP methods is selecting the kernel. In the deep kernel learning (DKL) paradigm, a deep neural network or "feature network" is used to map inputs into a latent feature space, where a GP with a "base kernel" acts; the resulting model is then trained in an end-to-end fashion. In this work, we introduce the "deep ensemble kernel learning" (DEKL) model which is a special case of DKL. In DEKL, a linear base kernel is used, enabling exact optimization of the base kernel hyperparameters and a scalable inference method that does not require approximation by inducing points. We also represent the feature network as a concatenation of an ensemble of learner networks with a common architecture, allowing for easy model parallelism. We show that DEKL is able to approximate any kernel if the number of learners in the ensemble is arbitrarily large. Comparing the DEKL model to DKL and deep ensemble (DE) baselines on both synthetic and real-world regression tasks, we find that DEKL often outperforms both baselines in terms of predictive performance.
Double Q-learning: New Analysis and Sharper Finite-time Bound
Authors: Lin Zhao, Huaqing Xiong, Yingbin Liang, Wei zhang
Date: 2021-01-01 00:00:00
Tasks: Q-Learning

Double Q-learning (Hasselt 2010) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning. However, theoretical understanding of double Q-learning is rather limited and the only existing finite-time analysis was recently established in (Xiong et al. 2020) under a polynomial learning rate. This paper analyzes the more challenging case with a rescaled linear learning rate for which the previous method does not appear to be applicable. We develop new analytical tools that achieve an order-level better finite-time convergence rate than the previously established result. Specifically, we show that synchronous double Q-learning attains an $\epsilon$-accurate global optimum with a time complexity of $\Omega\left(\frac{\ln D}{(1-\gamma)^6\epsilon^2} + \frac{\sqrt{\ln D}}{(1-\gamma)^7\epsilon^2} \right)$, and the asynchronous algorithm attains a time complexity of $\Omega\left(\frac{L^6\sqrt{\ln D}}{(1-\gamma)^7\epsilon^2} \right)$, where $D$ is the cardinality of the state-action space, $\gamma$ is the discount factor, and $L$ is a parameter related to the sampling strategy for asynchronous double Q-learning. These results improve the order-level dependence of the convergence rate on all major parameters $(\epsilon,1-\gamma, D, L)$ provided in (Xiong et al. 2020). The new analysis provided in this paper presents a more direct and succinct approach for characterizing the finite-time convergence rate of Double Q-learning.
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
Authors: Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles Ling, Boyu Wang
Date: 2021-01-01 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

We study the label shift problem in multi-source transfer learning and derive new generic principles to control the target generalization risk. Our theoretical grounded framework unifies the principles of conditional feature alignment, label distribution ratio estimation and domain relation weights estimation. Moreover, the inspired practice provides a unified algorithm for various multi-source label shift transfer scenarios: learning with limited target data, unsupervised domain adaptation and label partial unsupervised domain adaptation. We evaluate the proposed method on these scenarios by extensive experiments and show that our proposed algorithm can significantly outperform the baselines.
Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning
Authors: Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen
Date: 2021-01-01 00:00:00
Tasks: Continual Learning

Catastrophic forgetting occurs when a neural network is trained sequentially on multiple tasks – its weights will be continuously modified and as a result, the network will lose its ability in solving a previous task. Many studies have proposed different techniques to prevent a base learner from forgetting. This paper provides a new perspective and introduces Highway-Connection Classifier Networks (HCNs) to complement existing continual learning techniques. When implemented alone, HCNs exhibited strong robustness against forgetting; and when combined with continual learning techniques, the combined results outperformed their baselines. Furthermore, our experiments found that HCNs achieved competitive performances when only their shallowest layer was subjected to continual learning techniques; implying that continual learning tuning could be limited to specific layers.
Multi-Representation Ensemble in Few-Shot Learning
Authors: Qing Chen, Jian Zhang
Date: 2021-01-01 00:00:00
Tasks: Few-Shot Learning

Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation. A number of DNNs (e.g., ResNet, DenseNet) have shown that representations from the earlier layers can be beneficial. They improved performance by aggregating representations from different layers. In this work, we asked the question, besides forming an aggregation, whether these representations can be utilized directly with the classification layer(s) to obtain better performance. We started our quest to the answer by investigating the classifiers based on the representations from different layers and observed that these classifiers were diverse and many of their decisions were complementary to each other, hence having the potential to generate a better overall decision when combined. Following this observation, we propose an ensemble method that creates an ensemble of classifiers, each taking a representation from a different depth of a base DNN as the input. We tested this ensemble method in the setting of few-shot learning. Experiments were conducted on the mini-ImageNet and tieredImageNet datasets which are commonly used in the evaluation of few-shot learning methods. Our ensemble achieves the new state-of-the-art results for both datasets, comparing to previous regular and ensemble approaches.