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Title: An adaptive granularity clustering method based on hyper-ball Abstract: The purpose of cluster analysis is to classify elements according to their similarity. Its applications range from astronomy to bioinformatics and pattern recognition. Our method is based on the idea that the data with similar distribution f... |
Title: Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction Abstract: Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the num... |
Title: Do Residual Neural Networks discretize Neural Ordinary Differential Equations? Abstract: Neural Ordinary Differential Equations (Neural ODEs) are the continuous analog of Residual Neural Networks (ResNets). We investigate whether the discrete dynamics defined by a ResNet are close to the continuous one of a Neur... |
Title: A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension Abstract: Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension. Despite the core role of sparse logistic regression in statistics and machine... |
Title: Graph Structure Based Data Augmentation Method Abstract: In this paper, we propose a novel graph-based data augmentation method that can generally be applied to medical waveform data with graph structures. In the process of recording medical waveform data, such as electrocardiogram (ECG) or electroencephalogram ... |
Title: Continuous Generative Neural Networks Abstract: In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting. The architecture is inspired by DCGAN, with one fully connected layer, several convolutional layers and nonlinear activation funct... |
Title: Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) Abstract: In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Ph... |
Title: Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning Abstract: Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-perf... |
Title: Speaker Identification using Speech Recognition Abstract: The audio data is increasing day by day throughout the globe with the increase of telephonic conversations, video conferences and voice messages. This research provides a mechanism for identifying a speaker in an audio file, based on the human voice biome... |
Title: Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation Abstract: Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based machine learning p... |
Title: COFS: Controllable Furniture layout Synthesis Abstract: Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation. Many existing methods tackle this problem as a sequence generation problem which imposes a spe... |
Title: Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases Abstract: Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take... |
Title: Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection Abstract: Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). However, most popular deep AD detectors cannot prot... |
Title: The impact of memory on learning sequence-to-sequence tasks Abstract: The recent success of neural networks in machine translation and other fields has drawn renewed attention to learning sequence-to-sequence (seq2seq) tasks. While there exists a rich literature that studies classification and regression using s... |
Title: On the Robustness of Safe Reinforcement Learning under Observational Perturbations Abstract: Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL ... |
Title: Generalization bounds and algorithms for estimating conditional average treatment effect of dosage Abstract: We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying... |
Title: Learning Security Strategies through Game Play and Optimal Stopping Abstract: We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve ... |
Title: Evaluating Automated Driving Planner Robustness against Adversarial Influence Abstract: Evaluating the robustness of automated driving planners is a critical and challenging task. Although methodologies to evaluate vehicles are well established, they do not yet account for a reality in which vehicles with autono... |
Title: Heterogeneous Treatment Effects Estimation: When Machine Learning meets multiple treatment regime Abstract: In many scientific and engineering domains, inferring the effect of treatment and exploring its heterogeneity is crucial for optimization and decision making. In addition to Machine Learning based models (... |
Title: What are People Talking about in #BackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model Abstract: Minority groups have been using social media to organize social movements that create profound social impacts... |
Title: L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library Abstract: Despite being the third most popular language in India, the Marathi language lacks useful NLP resources. Moreover, popular NLP libraries do not have support for the Marathi language. With L3Cube-MahaNLP, we aim to build r... |
Title: Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds Abstract: We study stochastic zeroth order gradient and Hessian estimators for real-valued functions in $\mathbb{R}^n$. We show that, via taking finite difference along random orthogonal directions, the variance o... |
Title: A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare Abstract: Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for ex... |
Title: Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks Abstract: Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecoverable encrypted data, data leakage, and privacy concerns. The prompt detection of ransomware attacks is required to mini... |
Title: Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing Abstract: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts. This is especially true s... |
Title: Unfooling Perturbation-Based Post Hoc Explainers Abstract: Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary abo... |
Title: An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge Abstract: We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtained by solving optimization probl... |
Title: TransforMAP: Transformer for Memory Access Prediction Abstract: Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which task machine learning based methods are increasingly applied. Unlike pr... |
Title: Bayes Classification using an approximation to the Joint Probability Distribution of the Attributes Abstract: The Naive-Bayes classifier is widely used due to its simplicity, speed and accuracy. However this approach fails when, for at least one attribute value in a test sample, there are no corresponding traini... |
Title: Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret Abstract: The stochastic multi-armed bandit setting has been recently studied in the non-stationary regime, where the mean payoff of each action is a non-decreasing function of the number of rounds passed since it was last p... |
Title: End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment Abstract: Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods. This paper proposes a preliminary investigation on end-to-end machin... |
Title: Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning Abstract: Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector. By contrast, Transforme... |
Title: BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis Abstract: Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attr... |
Title: Last-iterate convergence analysis of stochastic momentum methods for neural networks Abstract: The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks. Current convergence results of stochastic momentum method... |
Title: TaSIL: Taylor Series Imitation Learning Abstract: We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in the higher-order Taylor series terms between the learned and expert policies. We sho... |
Title: Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding Abstract: Contrastive learning, especially Self-Supervised Contrastive Learning (SSCL), has achieved great success in extracting powerful features from unlabeled data, enabling comparable performance to the supervised counterpart. In this ... |
Title: Mitigating Out-of-Distribution Data Density Overestimation in Energy-Based Models Abstract: Deep energy-based models (EBMs), which use deep neural networks (DNNs) as energy functions, are receiving increasing attention due to their ability to learn complex distributions. To train deep EBMs, the maximum likelihoo... |
Title: Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods Abstract: While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. To address this issue, we investigate the... |
Title: Universality of group convolutional neural networks based on ridgelet analysis on groups Abstract: We investigate the approximation property of group convolutional neural networks (GCNNs) based on the ridgelet theory. We regard a group convolution as a matrix element of a group representation, and formulate a ve... |
Title: Bayesian Low-Rank Interpolative Decomposition for Complex Datasets Abstract: In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are... |
Title: Robust Weight Perturbation for Adversarial Training Abstract: Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation during network training by maximizing the classification loss on advers... |
Title: Lepton Flavour Violation Identification in Tau Decay ($\tau^{-} \rightarrow \mu^{-}\mu^{-}\mu^{+}$) Using Artificial Intelligence Abstract: The discovery of neutrino oscillation, proving that neutrinos do have masses, reveals the misfits of particles in the current Standard Model (SM) theory. In theory, neutrino... |
Title: Adaptive Learning for Discovery Abstract: In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide appli... |
Title: Temporal Multiresolution Graph Neural Networks For Epidemic Prediction Abstract: In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to... |
Title: Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning Abstract: To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, c... |
Title: GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization Abstract: Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given... |
Title: Adversarial Bandits Robust to $S$-Switch Regret Abstract: We study the adversarial bandit problem under $S$ number of switching best arms for unknown $S$. For handling this problem, we adopt the master-base framework using the online mirror descent method (OMD). We first provide a master-base algorithm with basi... |
Title: To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning Abstract: Federated learning (FL) facilitates collaboration between a group of clients who seek to train a common machine learning model without directly sharing their local data. Although there is an abundance of research o... |
Title: Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning Abstract: We study data poisoning attacks on online deep reinforcement learning (DRL) where the attacker is oblivious to the learning algorithm used by the agent and does not necessarily have full knowledge of the environment. We demonstrat... |
Title: Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression Abstract: As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improvements under different model and d... |
Title: Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning Abstract: Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such tasks are partially-observed; for exam... |
Title: Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning Abstract: More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, low... |
Title: Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering Abstract: The singular subspaces perturbation theory is of fundamental importance in probability and statistics. It has various applications across different fields. We consider two arbitrary matrices where one is a leave-one-column-ou... |
Title: Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina Abstract: Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over the... |
Title: Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors Abstract: Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we inte... |
Title: Exploring the Open World Using Incremental Extreme Value Machines Abstract: Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch ... |
Title: Daisy Bloom Filters Abstract: Weighted Bloom filters (Bruck, Gao and Jiang, ISIT 2006) are Bloom filters that adapt the number of hash functions according to the query element. That is, they use a sequence of hash functions $h_1, h_2, \dots$ and insert $x$ by setting the bits in $k_x$ positions $h_1(x), h_2(x), ... |
Title: FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation Abstract: Federated Learning (FL) is a decentralized learning paradigm in which multiple clients collaboratively train deep learning models without centralizing their local data and hence preserve data privacy. Real-world ap... |
Title: Confederated Learning: Federated Learning with Decentralized Edge Servers Abstract: Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server... |
Title: Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning Abstract: Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural... |
Title: A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing Abstract: Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the t... |
Title: ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection Abstract: Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting... |
Title: Unbalanced CO-Optimal Transport Abstract: Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by inferring an alignment between features as well. While this approach leads to better alignmen... |
Title: CalFAT: Calibrated Federated Adversarial Training with Label Skewness Abstract: Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks. To improve the adversarial robustness of FL, few federated adversarial training (FAT) methods have ... |
Title: Neural Volumetric Object Selection Abstract: We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates ... |
Title: Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review Abstract: Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has eme... |
Title: Harnessing spectral representations for subgraph alignment Abstract: With the rise and advent of graph learning techniques, graph data has become ubiquitous. However, while several efforts are being devoted to the design of new convolutional architectures, pooling or positional encoding schemes, less effort is b... |
Title: Detecting Unknown DGAs without Context Information Abstract: New malware emerges at a rapid pace and often incorporates Domain Generation Algorithms (DGAs) to avoid blocking the malware's connection to the command and control (C2) server. Current state-of-the-art classifiers are able to separate benign from mali... |
Title: Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles Abstract: Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attract... |
Title: Data-driven Numerical Invariant Synthesis with Automatic Generation of Attributes Abstract: We propose a data-driven algorithm for numerical invariant synthesis and verification. The algorithm is based on the ICE-DT schema for learning decision trees from samples of positive and negative states and implications ... |
Title: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem Abstract: Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning tasks. A natural follow-up question is how to abstract m... |
Title: AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications Abstract: Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based commu... |
Title: Dataset Condensation via Efficient Synthetic-Data Parameterization Abstract: The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive d... |
Title: FedAUXfdp: Differentially Private One-Shot Federated Distillation Abstract: Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients' data are heterogeneous. One promising approach to this challenge is the recently proposed method FedAUX, an augmentation of fe... |
Title: Sampling-free Inference for Ab-Initio Potential Energy Surface Networks Abstract: Obtaining the energy of molecular systems typically requires solving the associated Schr\"odinger equation. Unfortunately, analytical solutions only exist for single-electron systems, and accurate approximate solutions are expensiv... |
Title: Knowledge Distillation for 6D Pose Estimation by Keypoint Distribution Alignment Abstract: Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose ... |
Title: Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model Abstract: Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct ... |
Title: Fast Nonlinear Vector Quantile Regression Abstract: Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable $\mathrm{Y}$ given explanatory features $\boldsymbol{\mathrm{X}}$. A limitation of QR is that it is only defined for scalar target variables, due t... |
Title: A Continuous Time Framework for Discrete Denoising Models Abstract: We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chai... |
Title: Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets Abstract: Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon $T$ suffer $\Omega(\sqrt{T})$ regret... |
Title: Running the Dual-PQC GAN on noisy simulators and real quantum hardware Abstract: In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the ex... |
Title: Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Abstract: This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent... |
Title: Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification Abstract: Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, whic... |
Title: A reconfigurable integrated electronic tongue and its use in accelerated analysis of juices and wines Abstract: Potentiometric electronic tongues (ETs) leveraging trends in miniaturization and internet of things (IoT) bear promise for facile mobile chemical analysis of complex multicomponent liquids, such as bev... |
Title: Agnostic Physics-Driven Deep Learning Abstract: This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response... |
Title: Scalable Multi-Agent Model-Based Reinforcement Learning Abstract: Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed environments due to its cap... |
Title: Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word Abstract: In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic generative model that represents the symbol emer... |
Title: Neural Copula: A unified framework for estimating generic high-dimensional Copula functions Abstract: The Copula is widely used to describe the relationship between the marginal distribution and joint distribution of random variables. The estimation of high-dimensional Copula is difficult, and most existing solu... |
Title: RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch Abstract: Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that ... |
Title: Metrizing Fairness Abstract: We study supervised learning problems for predicting properties of individuals who belong to one of two demographic groups, and we seek predictors that are fair according to statistical parity. This means that the distributions of the predictions within the two groups should be close... |
Title: Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength Abstract: Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in... |
Title: Hilbert Curve Projection Distance for Distribution Comparison Abstract: Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance b... |
Title: A Transistor Operations Model for Deep Learning Energy Consumption Scaling Abstract: Deep Learning (DL) has transformed the automation of a wide range of industries and finds increasing ubiquity in society. The increasing complexity of DL models and its widespread adoption has led to the energy consumption doubl... |
Title: SEREN: Knowing When to Explore and When to Exploit Abstract: Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To encourage exploration, recent approaches proposed adding stochasticity to a... |
Title: Embedding Graphs on Grassmann Manifold Abstract: Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in... |
Title: Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences Abstract: Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale appli... |
Title: Improved Algorithms for Bandit with Graph Feedback via Regret Decomposition Abstract: The problem of bandit with graph feedback generalizes both the multi-armed bandit (MAB) problem and the learning with expert advice problem by encoding in a directed graph how the loss vector can be observed in each round of th... |
Title: CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning Abstract: Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering.... |
Title: Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories Abstract: The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growi... |
Title: CHALLENGER: Training with Attribution Maps Abstract: We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. Regularization is key in deep learning, especially when training complex models on relatively small datasets. In order to u... |
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