pwc-archive/papers-with-abstracts huggingface dataset:
On the Geometry of Deep Bayesian Active Learning
Authors: Xiaofeng Cao, Ivor Tsang
Date: 2021-01-01 00:00:00
Tasks: Active Learning

We present  geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its geometric interpretation interacting with a deep learning model. There are two main components in GBALD: initial acquisitions based on core-set construction and model uncertainty estimation with those initial acquisitions. Our key innovation is to construct the core-set on an ellipsoid, not typical sphere, preventing its updates towards the boundary regions of the distributions. Main improvements over BALD are twofold: relieving sensitivity to uninformative prior and reducing redundant information of model uncertainty. To guarantee the improvements, our generalization analysis proves that, compared to typical Bayesian  spherical interpretation, geodesic search with ellipsoid can derive a tighter lower error bound and achieve higher probability to obtain  a nearly zero error. Experiments on acquisitions with several scenarios demonstrate that, yielding slight perturbations to noisy and repeated samples,  GBALD further achieves significant accuracy improvements  than BALD, BatchBALD and other baselines.
ZCal: Machine learning methods for calibrating radio interferometric data
Authors: Simphiwe Zitha, Arun Aniyan, Oleg Smirnov, Risuna Nkolele
Date: 2021-01-01 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning

Calibration is the most critical data processing step needed for generating images of high dynamic range \citep{editioncasa}. With ever-increasing data volumes produced by modern radio telescopes \cite{aniyan2017classifying}, astronomers are overwhelmed by the amount of data that needs to be manually processed and analyzed using limited computational resources \citep{yatawatta2020stochastic}. Therefore, intelligent and automated systems are required to overcome these challenges. Traditionally, astronomers use a package such as Common Astronomy Software Applications (CASA) to compute the gain solutions based on regular observations of a known calibrator source \citep{thompson2017interferometry} \citep{abebe2015study} \citep{grobler2016calibration} \citep{editioncasa}. The traditional approach to calibration is iterative and time-consuming \citep{jajarmizadeh2017optimal}, thus, the proposal of machine learning techniques. The applications of machine learning have created an opportunity to deal with complex problems currently encountered in radio astronomy data processing \citep{aniyan2017classifying}. In this work, we propose the use of supervised machine learning models to first generation calibration (1GC), using the KAT-7 telescope environmental and pointing sensor data recorded during observations. Applying machine learning to 1GC, as opposed to calculating the gain solutions in CASA, has shown evidence of reducing computation, as well as accurately predicting the 1GC gain solutions and antenna behaviour. These methods are computationally less expensive, however they have not fully learned to generalise in predicting accurate 1GC solutions by looking at environmental and pointing sensors. We use an ensemble multi-output regression models based on random forest, decision trees, extremely randomized trees and K-nearest neighbor algorithms. The average prediction error obtained during the testing of our models on testing data is $ \approx 0.01 < rmse < 0.09$ for gain amplitude per antenna, and $0.2 rad < rmse <0.5 rad$ for gain phase. This shows that the instrumental parameters used to train our model strongly correlate with gain amplitude effects than a phase.
Reinforcement Learning for Control with Probabilistic Stability Guarantee
Authors: Minghao Han, Zhipeng Zhou, Lixian Zhang, Jun Wang, Wei Pan
Date: 2021-01-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable. However, in control theory, the stability of a closed-loop system can be hardly guaranteed using the policy/controller learned solely from samples. In this paper, we will combine Lyapunov's method in control theory and stochastic analysis to analyze the mean square stability of MDP in a model-free manner. Furthermore, the finite sample bounds on the probability of stability are derived as a function of the number M and length T of the sampled trajectories. And we show that there is a lower bound on T and the probability is much more demanding for M than T. Based on the theoretical results, a REINFORCE like algorithm is proposed to learn the controller and the Lyapunov function simultaneously.
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization
Authors: Jung Hyun Lee, Jihun Yun, Sung Ju Hwang, Eunho Yang
Date: 2021-01-01 00:00:00
Tasks: Quantization

Network quantization, which aims to reduce the bit-lengths of the network weights and activations, has emerged as one of the key ingredients to reduce the size of neural networks for their deployments to resource-limited devices. In order to overcome the nature of transforming continuous activations and weights to discrete ones, recent study called Relaxed Quantization (RQ) [Louizos et al. 2019] successfully employ the popular Gumbel-Softmax that allows this transformation with efficient gradient-based optimization. However, RQ with this Gumbel-Softmax relaxation still suffers from bias-variance trade-off depending on the temperature parameter of Gumbel-Softmax. To resolve the issue, we propose a novel method, Semi-Relaxed Quantization (SRQ) that uses multi-class straight-through estimator to effectively reduce the bias and variance, along with a new regularization technique, DropBits that replaces dropout regularization to randomly drop the bits instead of neurons to further reduce the bias of the multi-class straight-through estimator in SRQ. As a natural extension of DropBits, we further introduce the way of learning heterogeneous quantization levels to find proper bit-length for each layer using DropBits. We experimentally validate our method on various benchmark datasets and network architectures, and also support the quantized lottery ticket hypothesis: learning heterogeneous quantization levels outperforms the case using the same but fixed quantization levels from scratch.
Generative Fairness Teaching
Authors: Rongmei Lin, Hanjun Dai, Li Xiong, Wei Wei
Date: 2021-01-01 00:00:00
Tasks: Fairness

Increasing evidences has shown that data biases towards sensitive features such as gender or race are often inherited or even amplified by machine learning models. Recent advancements in fairness mitigate such biases by adjusting the predictions across sensitive groups during the training. Such a correction, however, can only take advantage of samples in a fixed dataset, which usually has limited amount of samples for the minority groups. We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training. We employ such a teaching strategy by implementing a Generative Fairness Teacher (GFT) that dynamically adjust the proportion of training data for a biased student model. Experimental results indicated that our teacher model is capable of guiding a wide range of biased models by improving  the fairness and performance trade-offs significantly.
Intragroup sparsity for efficient inference
Authors: Zilin Yu, Chao Wang, Xin Wang, Yong Zhao, Xundong Wu
Date: 2021-01-01 00:00:00

This work studies intragroup sparsity, a fine-grained structural constraint on network weight parameters. It eliminates the computational inefficiency of fine-grained sparsity due to irregular dataflow, while at the same time achieving high inference accuracy. We present theoretical analysis on how weight group sizes affect sparsification error, and on how the performance of pruned networks changes with sparsity level. Further, we analyze inference-time I/O cost of two different strategies for achieving intragroup sparsity and how the choice of strategies affect I/O cost under mild assumptions on accelerator architecture. Moreover, we present a novel training algorithm that yield models of improved accuracies over the standard training approach under the intragroup sparsity constraint.
Least Probable Disagreement Region for Active Learning
Authors: Seong Jin Cho, Gwangsu Kim, Chang D. Yoo
Date: 2021-01-01 00:00:00
Tasks: Active Learning

Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable. This paper defines a theoretical distance of unlabeled sample to the decision boundary as the least probable disagreement region (LPDR) containing the unlabeled sample, and it discusses how this theoretical distance can be empirically evaluated with a lower order of time complexity. Monte Carlo sampling of the hypothesis is performed in approximating the theoretically defined distance. Experimental results on various datasets show that the proposed algorithm consistently outperforms all other high performing uncertainty based active learning algorithms and leads to state-of-the-art active learning performance on CIFAR10, CIFAR100, Tiny ImageNet and Food101 datasets. Only the proposed algorithm outperforms random sampling on CIFAR100 dataset using K-CNN while all other algorithms fail to do so.
Symmetry Control Neural Networks
Authors: Marc Syvaeri, Sven Krippendorf
Date: 2021-01-01 00:00:00

This paper continues the quest for designing the optimal physics bias for neural networks predicting the dynamics of systems when the underlying dynamics shall be inferred from the data directly. The description of physical systems is greatly simplified when the underlying symmetries of the system are taken into account. In classical systems described via Hamiltonian dynamics this is achieved by using appropriate coordinates, so-called cyclic coordinates, which reveal conserved quantities directly. Without changing the Hamiltonian, these coordinates can be obtained via canonical transformations. We show that such coordinates can be searched for automatically with appropriate loss functions which naturally arise from Hamiltonian dynamics. As a proof of principle, we test our method on standard classical physics systems where our network identifies the conserved quantities in an unsupervised way and find improved performance on predicting the dynamics of the system compared to networks biasing just to the Hamiltonian. Effectively, these new coordinates guarantee that motion takes place on symmetry orbits in phase space, i.e. appropriate lower dimensional sub-spaces of phase space. By fitting analytic formulae we recover that our networks are utilising conserved quantities such as (angular) momentum.
Federated Averaging as Expectation Maximization
Authors: Christos Louizos, Matthias Reisser, Joseph Soriaga, Max Welling
Date: 2021-01-01 00:00:00
Tasks: Federated Learning

Federated averaging (FedAvg), despite its simplicity, has been the main approach in training neural networks in the federated learning setting. In this work, we show that the algorithmic choices of the FedAvg algorithm correspond to optimizing a single objective function that involves the global and all of the shard specific models using a hard version of the well known Expectation-Maximization (EM) algorithm. As a result, we gain a better understanding of the behavior and design choices of federated averaging while being able to provide interesting connections to recent literature. Based on this view, we further propose FedSparse, a version of federated averaging that employs prior distributions to promote model sparsity. In this way, we obtain a procedure that leads to reductions in both server-client and client-server communication costs as well as more efficient models.
Differentiable Approximations for Multi-resource Spatial Coverage Problems
Authors: Nitin Kamra, Yan Liu
Date: 2021-01-01 00:00:00

Resource allocation for coverage of physical spaces is a challenging problem in robotic surveillance, mobile sensor networks and security domains. Recent gradient-based optimization approaches to this problem estimate utilities of actions by using neural networks to learn a differentiable approximation to spatial coverage objectives. In this work, we empirically show that spatial coverage objectives with multiple-resources are combinatorially hard to approximate for neural networks and lead to sub-optimal policies. As our major contribution, we propose a tractable framework to approximate a general class of spatial coverage objectives and their gradients using a combination of Newton-Leibniz theorem, spatial discretization and implicit boundary differentiation. We empirically demonstrate the efficacy of our proposed framework on single and multi-agent spatial coverage problems.
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
Authors: Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-Eung Kim
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Management, Reinforcement Learning (RL)

Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exempliﬁed by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked ﬁrst in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efﬁciency in every test scenarios. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids.
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
Authors: Jonas Fischer, Anna Oláh, Jilles Vreeken
Date: 2021-01-01 00:00:00

We propose a novel method for exploring how neurons within a neural network interact. In particular, we consider activation values of a network for given data, and propose to mine noise-robust rules of the form $X \rightarrow Y$ , where $X$ and $Y$ are sets of neurons in different layers. To ensure we obtain a small and non-redundant set of high quality rules, we formalize the problem in terms of the Minimum Description Length principle, by which we identify the best set of rules as the one that best compresses the activation data. To discover good rule sets, we propose the unsupervised ExplaiNN algorithm. Extensive evaluation shows that our rules give clear insight in how networks perceive the world: they identify shared, resp. class-specific traits, compositionality within the network, as well as locality in convolutional layers. Our rules are easily interpretable, but also super-charge prototyping as they identify which groups of neurons to consider in unison.
HyperDynamics: Generating Expert Dynamics Models by Observation
Authors: Zhou Xian, Shamit Lal, Hsiao-Yu Tung, Emmanouil Antonios Platanios, Katerina Fragkiadaki
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Attribute

We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environment that are not part of the low-dimensional state yet affect its temporal dynamics are inferred from the interaction history and visual observations, and are implicitly captured in the generated parameters. We test HyperDynamics on a set of object pushing and locomotion tasks. It outperforms existing dynamics models in the literature that adapt to environment variations by learning dynamics over high dimensional visual observations, capturing the interactions of the agent in recurrent state representations, or using gradient-based meta-optimization. We also show our method matches the performance of an ensemble of separately trained experts, while also being able to generalize well to unseen environment variations at test time. We attribute its good performance to the multiplicative interactions between the inferred system properties—captured in the generated parameters—and the low-dimensional state representation of the dynamical system.
Probabilistic Meta-Learning for Bayesian Optimization
Authors: Felix Berkenkamp, Anna Eivazi, Lukas Grossberger, Kathrin Skubch, Jonathan Spitz, Christian Daniel, Stefan Falkner
Date: 2021-01-01 00:00:00
Tasks: Bayesian Optimization, Meta-Learning, Transfer Learning

Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem. For applications that depend on uncertainty estimates, e.g., in Bayesian optimization, recent probabilistic approaches have shown good performance at test time, but either scale poorly with the number of data points or under-perform with little data on the test task. In this paper, we propose a novel approach to probabilistic transfer learning that uses a generative model for the underlying data distribution and simultaneously learns a latent feature distribution to represent unknown task properties. To enable fast and accurate inference at test-time, we introduce a novel meta-loss that structures the latent space to match the prior used for inference. Together, these contributions ensure that our probabilistic model exhibits high sample-efficiency and provides well-calibrated uncertainty estimates. We evaluate the proposed approach and compare its performance to probabilistic models from the literature on a set of Bayesian optimization transfer-learning tasks.
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs
Authors: Gabrielle Ras, Luca Ambrogioni, Pim Haselager, Marcel van Gerven, Umut Güçlü
Date: 2021-01-01 00:00:00
Tasks: Action Recognition

Current deep learning architectures that make use of the 3D convolution (3DConv) achieve state-of-the-art results on action recognition benchmarks. However, the 3DConv does not easily lend itself to explainable model decisions. To this end we introduce a novel and intrinsic approach, whereby all the aspects of the 3DConv are rendered explainable. Our approach proposes the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3DConv. In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters. We demonstrate that 3TConv learns temporal transformations that afford a direct interpretation by analyzing the transformation parameter statistics on a model level. Our experiments show that in the low-data regime the 3TConv outperforms 3DConv and R(2+1)D while containing up to 77\% less parameters.
Pair-based Self-Distillation for Semi-supervised Domain Adaptation
Authors: Jeongbeen Yoon, Dahyun Kang, Minsu Cho
Date: 2021-01-01 00:00:00
Tasks: Domain Adaptation, Semi-supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain. In this paper, we propose a pair-based SSDA method that adapts a learner to the target domain using self-distillation with sample pairs. Our method composes the sample pair by selecting a teacher sample from a labeled dataset (i.e., source or labeled target) and its student sample from an unlabeled dataset (i.e., unlabeled target), and then minimizes the output discrepancy between the two samples. We assign a reliable student to a teacher using pseudo-labeling and reliability evaluation so that the teacher sample propagates its prediction to the corresponding student sample. When the teacher sample is chosen from the source dataset, it minimizes the discrepancy between the source domain and the target domain. When the teacher sample is selected from the labeled target dataset, it reduces the discrepancy within the target domain. Experimental evaluation on standard benchmarks shows that our method effectively minimizes both the inter-domain and intra-domain discrepancies, thus achieving the state-of-the-art results.
Learning from deep model via exploring local targets
Authors: Wenxian Shi, Yuxuan Song, Hao Zhou, Bohan Li, Lei LI
Date: 2021-01-01 00:00:00
Tasks: Knowledge Distillation, model

Deep neural networks often have huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge distillation is one approach to derive compact models from bigger ones. 
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.  In this paper, we propose proKT, a new model-agnostic method by projecting the supervision signals of a teacher model into the student's parameter space. Such projection is implemented by decomposing the training objective  into  local intermediate targets with approximate mirror descent technique. The proposed method could be less sensitive with the quirks during optimization which could result in a better local optima. Experiments on both image and text datasets show that our proposed proKT consistently achieves the state-of-the-art performance comparing to all existing knowledge distillation methods.
Local Clustering Graph Neural Networks
Authors: Jiezhong Qiu, Yukuo Cen, Qibin Chen, Chang Zhou, Jingren Zhou, Hongxia Yang, Jie Tang
Date: 2021-01-01 00:00:00
Tasks: Clustering, GPU

Graph Neural Networks (GNNs), which benefit various real-world problems and applications, have emerged as a powerful technique for learning graph representations. The depth of a GNN model, denoted by $K$, restricts the receptive field of a node to its $K$-hop neighbors and plays a subtle role in the performance of GNNs. Recent works demonstrate how different choices of $K$ produce a trade-off between increasing representation capacity and avoiding over-smoothing. We establish a theoretical connection between GNNs and local clustering, showing that short random-walks in GNNs have a high probability to be stuck at a local cluster. Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference. Compared to full-batch GNNs, sampling-based GNNs and graph partition-based GNNs, LCGNN performs comparably or even better, achieving state-of-the-art results on four Open Graph Benchmark (OGB) datasets. The locality of LCGNN allows it to scale to graphs with 100M nodes and 1B edges on a single GPU.