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Title: Investigating and Explaining the Frequency Bias in Image Classification Abstract: CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency componen... |
Title: Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis Abstract: Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e.... |
Title: How to Minimize the Weighted Sum AoI in Multi-Source Status Update Systems: OMA or NOMA? Abstract: In this paper, the minimization of the weighted sum average age of information (AoI) in a multi-source status update communication system is studied. Multiple independent sources send update packets to a common des... |
Title: Fast Rate Generalization Error Bounds: Variations on a Theme Abstract: A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the relevant works, the convergence rate of the expected generalizatio... |
Title: SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning Abstract: In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are able to transfer skills and knowledge. If we can cycle to work and drive to the store, we can also cycl... |
Title: Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors Abstract: Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate response to a st... |
Title: Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances Abstract: Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail... |
Title: Controlled Dropout for Uncertainty Estimation Abstract: Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictio... |
Title: LPGNet: Link Private Graph Networks for Node Classification Abstract: Classification tasks on labeled graph-structured data have many important applications ranging from social recommendation to financial modeling. Deep neural networks are increasingly being used for node classification on graphs, wherein nodes ... |
Title: Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery Abstract: The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture doma... |
Title: Federated Learning with Noisy User Feedback Abstract: Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the clou... |
Title: PTFlash : A deep learning framework for isothermal two-phase equilibrium calculations Abstract: Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computational time. In this work, we introduce... |
Title: Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures Abstract: This paper deals with the taking into account a given set of realizations as constraints in the Kullback-Leibler minimum principle, which is used as a probabilistic learning algorith... |
Title: Beyond backpropagation: implicit gradients for bilevel optimization Abstract: This paper reviews gradient-based techniques to solve bilevel optimization problems. Bilevel optimization is a general way to frame the learning of systems that are implicitly defined through a quantity that they minimize. This charact... |
Title: RCMNet: A deep learning model assists CAR-T therapy for leukemia Abstract: Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, ... |
Title: Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization Abstract: Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this problem, we develop an efficient solver for... |
Title: Incremental Data-Uploading for Full-Quantum Classification Abstract: The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding high dimensio... |
Title: Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation Abstract: Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique inst... |
Title: Quantification of Robotic Surgeries with Vision-Based Deep Learning Abstract: Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term pat... |
Title: A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition Abstract: Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to perform well in general, they are not easy to m... |
Title: Generative Adversarial Neural Operators Abstract: We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled from infinite-... |
Title: Differentially Private Generalized Linear Models Revisited Abstract: We study the problem of $(\epsilon,\delta)$-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not nec... |
Title: Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design Abstract: Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While e... |
Title: Functional2Structural: Cross-Modality Brain Networks Representation Learning Abstract: MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph min... |
Title: Learning Optimal Propagation for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because ... |
Title: Network Gradient Descent Algorithm for Decentralized Federated Learning Abstract: We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the ... |
Title: Optimally tackling covariate shift in RKHS-based nonparametric regression Abstract: We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We focus on two natural families of covariate shift problems defined using the likelihood ratios betw... |
Title: Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports Abstract: Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consum... |
Title: IMU Based Deep Stride Length Estimation With Self-Supervised Learning Abstract: Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit cal... |
Title: Variance Reduction based Partial Trajectory Reuse to Accelerate Policy Gradient Optimization Abstract: Built on our previous study on green simulation assisted policy gradient (GS-PG) focusing on trajectory-based reuse, in this paper, we consider infinite-horizon Markov Decision Processes and create a new import... |
Title: Large Scale Transfer Learning for Differentially Private Image Classification Abstract: Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Private Stochastic Gradient Descent (DP-SGD) has ... |
Title: Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery Abstract: Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerat... |
Title: Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations Abstract: We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning t... |
Title: Low Dimensional Invariant Embeddings for Universal Geometric Learning Abstract: This paper studies separating invariants: mappings on $d$-dimensional semi-algebraic subsets of $D$ dimensional Euclidean domains which are invariant to semi-algebraic group actions and separate orbits. The motivation for this study ... |
Title: Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing Abstract: We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in a... |
Title: Over-The-Air Federated Learning under Byzantine Attacks Abstract: Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global model. FL allows the clients to participate in the training phase, g... |
Title: A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior Abstract: Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach re... |
Title: GANs as Gradient Flows that Converge Abstract: This paper approaches the unsupervised learning problem by gradient descent in the space of probability density functions. Our main result shows that along the gradient flow induced by a distribution-dependent ordinary differential equation (ODE), the unknown data d... |
Title: GreenDB: Toward a Product-by-Product Sustainability Database Abstract: The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommend... |
Title: Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes Abstract: This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a tria... |
Title: Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks Abstract: Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial conditions and boundary co... |
Title: New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography Abstract: Undiagnosed diabetes is present in 21.4% of adults with diabetes. Diabetes can remain asymptomatic and undetected due to limitations in screening rates. To address this issue, questionnaires, such as the American D... |
Title: Evaluating Context for Deep Object Detectors Abstract: Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (... |
Title: Multi-confound regression adversarial network for deep learning-based diagnosis on highly heterogenous clinical data Abstract: Automated disease detection in medical images using deep learning holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contai... |
Title: Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands Abstract: Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multi-reference (MR) charact... |
Title: Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics Abstract: Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the initial contact points of the end effectors a... |
Title: Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction Abstract: This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is r... |
Title: Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning Abstract: In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed accordi... |
Title: Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations Abstract: We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding. Our method encodes the v... |
Title: Rapid Locomotion via Reinforcement Learning Abstract: Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m/s. This system runs and tu... |
Title: Generative methods for sampling transition paths in molecular dynamics Abstract: Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastab... |
Title: Morphological Wobbling Can Help Robots Learn Abstract: We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance. We consider quantities such as mass, actuator strength, and size that are usually fixed in a robot, and show that when those quantitie... |
Title: Quantum Extremal Learning Abstract: We propose a quantum algorithm for `extremal learning', which is the process of finding the input to a hidden function that extremizes the function output, without having direct access to the hidden function, given only partial input-output (training) data. The algorithm, call... |
Title: Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data Abstract: The analysis of electrocardiogram (ECG) signals can be time consuming as it is performed manually by cardiologists. Therefore, automation through machine learning (ML) classification is being increasingly propos... |
Title: Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI Abstract: Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In ... |
Title: A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus Abstract: In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. ... |
Title: Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity Abstract: Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation... |
Title: Finding Bipartite Components in Hypergraphs Abstract: Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ ... |
Title: Spiking Graph Convolutional Networks Abstract: Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be ... |
Title: General sum stochastic games with networked information flows Abstract: Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these domains: 1) network-structured player interactions, 2) pair-... |
Title: REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics Abstract: Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learni... |
Title: Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League Abstract: Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of tech... |
Title: Communication-Efficient Adaptive Federated Learning Abstract: Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of federated learning in practice still faces numerous challenges, such as th... |
Title: Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities Abstract: The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that wil... |
Title: REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research Abstract: Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machi... |
Title: Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Abstract: We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernels by interpolating between meta-learning and conventional deep kernel learning. Our approa... |
Title: Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case Abstract: In this work, a multi-stage Machine Learning (ML) pipeline is proposed for pipe leakage detection in an industrial environment. As opposed to other industrial and urban environments, the environment under study inc... |
Title: Mode Reduction for Markov Jump Systems Abstract: Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time. To achieve accurate modeling in practice, one may need a large number of modes, but this may in turn increase the model complexity drastically. Existing... |
Title: KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design Abstract: Knitted fabric exhibits avalanche-like events when deformed: by analogy with eathquakes, we are interested in predicting these "knitquakes". However, as in most analogous seismic mode... |
Title: Chemoreception and chemotaxis of a three-sphere swimmer Abstract: The coupled problem of hydrodynamics and solute transport for the Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number set to zero and P\'eclet numbers (Pe) ranging from 0.06 to 60. The adopted method is the numerical simul... |
Title: LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding Abstract: This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods... |
Title: On Disentangled and Locally Fair Representations Abstract: We study the problem of performing classification in a manner that is fair for sensitive groups, such as race and gender. This problem is tackled through the lens of disentangled and locally fair representations. We learn a locally fair representation, s... |
Title: Multi-Agent Deep Reinforcement Learning in Vehicular OCC Abstract: Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim ... |
Title: What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning Abstract: Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relative posit... |
Title: Unsupervised Mismatch Localization in Cross-Modal Sequential Data Abstract: Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment alg... |
Title: LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks Abstract: Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run... |
Title: Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications Abstract: Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, sol... |
Title: Can collaborative learning be private, robust and scalable? Abstract: We investigate the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples in train- and inference-time attacks. We explore the applications of... |
Title: PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data Abstract: Most real-world ecological dynamics, ranging from ecosystem dynamics to collective animal movement, are inherently stochastic in nature. Stochastic differential equations (SDEs) are a popular modelling framewo... |
Title: Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization Abstract: Decision making algorithms are used in a multitude of different applications. Conventional approaches for designing decision algorithms employ principled and simplified modelling, based on which one can determine decisions... |
Title: Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks Abstract: Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability ... |
Title: ST-ExpertNet: A Deep Expert Framework for Traffic Prediction Abstract: Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide level is in a mixed state with several basic patterns (e.g., comm... |
Title: GANimator: Neural Motion Synthesis from a Single Sequence Abstract: We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel an... |
Title: Contrastive Multi-view Hyperbolic Hierarchical Clustering Abstract: Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clus... |
Title: Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems Abstract: Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness... |
Title: AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching Abstract: This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challen... |
Title: Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims Abstract: We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pand... |
Title: PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis Abstract: A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes. A non-negative latent factor (NL... |
Title: A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning Abstract: Deep reinforcement learning (DRL) has made significant achievements in many real-world applications. But these real-world applications typically can only provide partial observations for making decisions due to occlusions and noisy senso... |
Title: One Size Does Not Fit All: The Case for Personalised Word Complexity Models Abstract: Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acqu... |
Title: LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning Abstract: Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or va... |
Title: Automated Imbalanced Classification via Layered Learning Abstract: In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instan... |
Title: One-way Explainability Isn't The Message Abstract: Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the d... |
Title: Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness Abstract: While deep neural networks are sensitive to adversarial noise, sparse coding using the Basis Pursuit (BP) method is robust against such attacks, including its multi-layer extensions. We prove that the stability theorem of BP h... |
Title: Deep Neural Network approaches for Analysing Videos of Music Performances Abstract: This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces se... |
Title: View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering Abstract: Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further... |
Title: FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework Abstract: Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quick... |
Title: DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation Abstract: Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches ten... |
Title: dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training Abstract: Distributed training using multiple devices (e.g., GPUs) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linea... |
Title: Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks Abstract: Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance. However, a pre-trained teacher mo... |
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