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Title: Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks Abstract: With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior... |
Title: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service Abstract: Graphs are widely used to model the complex relationships among entities. As a powerful tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention due to its end-to-end processing capabilit... |
Title: Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems Abstract: Reliability is an important tool for evaluating the performance of modern networks. Currently, it is NP-hard and #P-hard to calculate the exact ... |
Title: Deeply-Supervised Knowledge Distillation Abstract: Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model. However, in the traditional knowledge distillation, teacher predictions are only used to provide the su... |
Title: The NLP Task Effectiveness of Long-Range Transformers Abstract: Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity. This has led to Transformer variants seeking to lessen computational complexity, such as Longformer and Performer. While such models have theoret... |
Title: Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series Abstract: Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies ... |
Title: Data-Driven Minimax Optimization with Expectation Constraints Abstract: Attention to data-driven optimization approaches, including the well-known stochastic gradient descent method, has grown significantly over recent decades, but data-driven constraints have rarely been studied, because of the computational ch... |
Title: IPD:An Incremental Prototype based DBSCAN for large-scale data with cluster representatives Abstract: DBSCAN is a fundamental density-based clustering technique that identifies any arbitrary shape of the clusters. However, it becomes infeasible while handling big data. On the other hand, centroid-based clusterin... |
Title: Online Control of Unknown Time-Varying Dynamical Systems Abstract: We study online control of time-varying linear systems with unknown dynamics in the nonstochastic control model. At a high level, we demonstrate that this setting is \emph{qualitatively harder} than that of either unknown time-invariant or known ... |
Title: A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications Abstract: Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to Pretrained Graph Models (PGMs). Owing to... |
Title: Aryl: An Elastic Cluster Scheduler for Deep Learning Abstract: Companies build separate training and inference GPU clusters for deep learning, and use separate schedulers to manage them. This leads to problems for both training and inference: inference clusters have low GPU utilization when the traffic load is l... |
Title: Cross-Modal Common Representation Learning with Triplet Loss Functions Abstract: Common representation learning (CRL) learns a shared embedding between two or more modalities to improve in a given task over using only one of the modalities. CRL from different data types such as images and time-series data (e.g.,... |
Title: When Does A Spectral Graph Neural Network Fail in Node Classification? Abstract: Spectral Graph Neural Networks (GNNs) with various graph filters have received extensive affirmation due to their promising performance in graph learning problems. However, it is known that GNNs do not always perform well. Although ... |
Title: Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesis Abstract: End-to-end singing voice synthesis (SVS) is attractive due to the avoidance of pre-aligned data. However, the auto learned alignment of singing voice with lyrics is difficult to match the durat... |
Title: Can Deep Learning be Applied to Model-Based Multi-Object Tracking? Abstract: Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense system... |
Title: Clustering Enabled Few-Shot Load Forecasting Abstract: While the advanced machine learning algorithms are effective in load forecasting, they often suffer from low data utilization, and hence their superior performance relies on massive datasets. This motivates us to design machine learning algorithms with impro... |
Title: Meta Knowledge Distillation Abstract: Recent studies pointed out that knowledge distillation (KD) suffers from two degradation problems, the teacher-student gap and the incompatibility with strong data augmentations, making it not applicable to training state-of-the-art models, which are trained with advanced au... |
Title: TimeREISE: Time-series Randomized Evolving Input Sample Explanation Abstract: Deep neural networks are one of the most successful classifiers across different domains. However, due to their limitations concerning interpretability their use is limited in safety critical context. The research field of explainable ... |
Title: Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap and Adaptive Residual Selection Abstract: Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for plannin... |
Title: On a Variance Reduction Correction of the Temporal Difference for Policy Evaluation in the Stochastic Continuous Setting Abstract: This paper deals with solving continuous time, state and action optimization problems in stochastic settings, using reinforcement learning algorithms, and considers the policy evalua... |
Title: GAN Estimation of Lipschitz Optimal Transport Maps Abstract: This paper introduces the first statistically consistent estimator of the optimal transport map between two probability distributions, based on neural networks. Building on theoretical and practical advances in the field of Lipschitz neural networks, w... |
Title: On loss functions and evaluation metrics for music source separation Abstract: We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most representative audio source separation losses we identified, ... |
Title: Out-Of-Distribution Generalization on Graphs: A Survey Abstract: Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the I.I.D. hypothesis, i.e., testing and training graph da... |
Title: Improved analysis of randomized SVD for top-eigenvector approximation Abstract: Computing the top eigenvectors of a matrix is a problem of fundamental interest to various fields. While the majority of the literature has focused on analyzing the reconstruction error of low-rank matrices associated with the retrie... |
Title: Planckian jitter: enhancing the color quality of self-supervised visual representations Abstract: Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The set of used data augmentations is of crucial importance for t... |
Title: Branching Reinforcement Learning Abstract: In this paper, we propose a novel Branching Reinforcement Learning (Branching RL) model, and investigate both Regret Minimization (RM) and Reward-Free Exploration (RFE) metrics for this model. Unlike standard RL where the trajectory of each episode is a single $H$-step ... |
Title: DeepTx: Deep Learning Beamforming with Channel Prediction Abstract: Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to pro... |
Title: Deep Koopman Operator with Control for Nonlinear Systems Abstract: Recently Koopman operator has become a promising data-driven tool to facilitate real-time control for unknown nonlinear systems. It maps nonlinear systems into equivalent linear systems in embedding space, ready for real-time linear control metho... |
Title: Should You Mask 15% in Masked Language Modeling? Abstract: Masked language models conventionally use a masking rate of 15% due to the belief that more masking would provide insufficient context to learn good representations, and less masking would make training too expensive. Surprisingly, we find that masking u... |
Title: Toward Development of Machine Learned Techniques for Production of Compact Kinetic Models Abstract: Chemical kinetic models are an essential component in the development and optimisation of combustion devices through their coupling to multi-dimensional simulations such as computational fluid dynamics (CFD). Low-... |
Title: Diagnosing Batch Normalization in Class Incremental Learning Abstract: Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermediate feature maps and has been widely validated to improve train... |
Title: No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices Abstract: Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model can be trained on any participating client. Ho... |
Title: Explainability of Predictive Process Monitoring Results: Can You See My Data Issues? Abstract: Predictive business process monitoring (PPM) has been around for several years as a use case of process mining. PPM enables foreseeing the future of a business process through predicting relevant information about how ... |
Title: Learning to Generalize across Domains on Single Test Samples Abstract: We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in ... |
Title: HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing Abstract: Classification of time series data is an important task for many application domains. One of the best existing methods for this task, in terms of accuracy and computation time, is MiniROCKET. In this wo... |
Title: Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Abstract: Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i.e., Gra... |
Title: Learning a Single Neuron for Non-monotonic Activation Functions Abstract: We study the problem of learning a single neuron $\mathbf{x}\mapsto \sigma(\mathbf{w}^T\mathbf{x})$ with gradient descent (GD). All the existing positive results are limited to the case where $\sigma$ is monotonic. However, it is recently ... |
Title: A Polyhedral Study of Lifted Multicuts Abstract: Fundamental to many applications in data analysis are the decompositions of a graph, i.e. partitions of the node set into component-inducing subsets. One way of encoding decompositions is by multicuts, the subsets of those edges that straddle distinct components. ... |
Title: On Measuring Excess Capacity in Neural Networks Abstract: We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class -- in our case, Rademacher complexity -- how much can we (a-priori) constrain this class while ... |
Title: Extended Unconstrained Features Model for Exploring Deep Neural Collapse Abstract: The modern strategy for training deep neural networks for classification tasks includes optimizing the network's weights even after the training error vanishes to further push the training loss toward zero. Recently, a phenomenon ... |
Title: Latent Outlier Exposure for Anomaly Detection with Contaminated Data Abstract: Anomaly detection aims at identifying data points that show systematic deviations from the majority of data in an unlabeled dataset. A common assumption is that clean training data (free of anomalies) is available, which is often viol... |
Title: Measuring Unintended Memorisation of Unique Private Features in Neural Networks Abstract: Neural networks pose a privacy risk to training data due to their propensity to memorise and leak information. Focusing on image classification, we show that neural networks also unintentionally memorise unique features eve... |
Title: Using Navigational Information to Learn Visual Representations Abstract: Children learn to build a visual representation of the world from unsupervised exploration and we hypothesize that a key part of this learning ability is the use of self-generated navigational information as a similarity label to drive a le... |
Title: Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients Abstract: Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference. However, current ... |
Title: A data-driven approach for learning to control computers Abstract: It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-scale expert demonstrations and human judgements of interactive beha... |
Title: A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation Abstract: Recent advances in 5G wireless technology and socioeconomic transformation have brought a paradigm shift in sensor a... |
Title: Modular multi-source prediction of drug side-effects with DruGNN Abstract: Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on dru... |
Title: Self-Supervised Class-Cognizant Few-Shot Classification Abstract: Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classificatio... |
Title: Domain Adaptive Fake News Detection via Reinforcement Learning Abstract: With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivia... |
Title: Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module Abstract: State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data, standard TTS mo... |
Title: Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model Abstract: Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierarchical word-and-ch... |
Title: Distributed k-Means with Outliers in General Metrics Abstract: Center-based clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is undoubtedly the k-means problem, which, given a set $P$ of points from a metric space and a parameter $k<|P|$, requires to determine a su... |
Title: Towards Battery-Free Machine Learning and Inference in Underwater Environments Abstract: This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant i... |
Title: GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Abstract: Online forums that allow participatory engagement between users have been transformative for public discussion of important issues. However, debates on such forums can sometimes escalate into full blown e... |
Title: Bias and unfairness in machine learning models: a systematic literature review Abstract: One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness ... |
Title: Generative modeling with projected entangled-pair states Abstract: We argue and demonstrate that projected entangled-pair states (PEPS) outperform matrix product states significantly for the task of generative modeling of datasets with an intrinsic two-dimensional structure such as images. Our approach builds on... |
Title: Geometry of the Minimum Volume Confidence Sets Abstract: Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement learning algorithms. This paper studies the geometry of the minimum-volume ... |
Title: An Intrusion Response System utilizing Deep Q-Networks and System Partitions Abstract: Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection... |
Title: The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining Abstract: The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about pr... |
Title: Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Abstract: This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerb... |
Title: Analysis of Random Sequential Message Passing Algorithms for Approximate Inference Abstract: We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student-teacher scenario. To model nontrivial dependencies between the la... |
Title: SemiRetro: Semi-template framework boosts deep retrosynthesis prediction Abstract: Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalab... |
Title: Quantum Lazy Training Abstract: In the training of over-parameterized model functions via gradient descent, sometimes the parameters do not change significantly and remain close to their initial values. This phenomenon is called lazy training, and motivates consideration of the linear approximation of the model ... |
Title: Data Augmentation for Deep Graph Learning: A Survey Abstract: Graph neural networks, as powerful deep learning tools to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To counter the data noise and data scarcity issues in deep graph learning (DGL), increasi... |
Title: Using the left Gram matrix to cluster high dimensional data Abstract: For high dimensional data, where P features for N objects (P >> N) are represented in an NxP matrix X, we describe a clustering algorithm based on the normalized left Gram matrix, G = XX'/P. Under certain regularity conditions, the rows in G t... |
Title: A multi-reconstruction study of breast density estimation using Deep Learning Abstract: Breast density estimation is one of the key tasks in recognizing individuals predisposed to breast cancer. It is often challenging because of low contrast and fluctuations in mammograms' fatty tissue background. Most of the t... |
Title: On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers Abstract: Algorithmic fairness literature presents numerous mathematical notions and metrics, and also points to a tradeoff between them while satisficing some or all of them simultaneousl... |
Title: Low-Rank Phase Retrieval with Structured Tensor Models Abstract: We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signals. Existing solutions involve recovering a matrix constructed by vec... |
Title: Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation Abstract: Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. Howeve... |
Title: Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning Abstract: Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefa... |
Title: XAI in the context of Predictive Process Monitoring: Too much to Reveal Abstract: Predictive Process Monitoring (PPM) has been integrated into process mining tools as a value-adding task. PPM provides useful predictions on the further execution of the running business processes. To this end, machine learning-bas... |
Title: Open-Ended Reinforcement Learning with Neural Reward Functions Abstract: Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches,... |
Title: More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors Abstract: Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effective... |
Title: Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks Abstract: The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MinINF problem for optimal social distancing involves minimizing the expected number of infections, when we are allowed to brea... |
Title: The learning phases in NN: From Fitting the Majority to Fitting a Few Abstract: The learning dynamics of deep neural networks are subject to controversy. Using the information bottleneck (IB) theory separate fitting and compression phases have been put forward but have since been heavily debated. We approach lea... |
Title: Efficient Distributed Machine Learning via Combinatorial Multi-Armed Bandits Abstract: We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among $n$ workers from which at most $b \leq n$ can be utilized in parallel. By assigning tasks to all the wo... |
Title: Towards Verifiable Federated Learning Abstract: Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misb... |
Title: Single Trajectory Nonparametric Learning of Nonlinear Dynamics Abstract: Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic expected $l^2$-distance bounds between the LSE and the true regression fun... |
Title: Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams Abstract: Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theor... |
Title: TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery Abstract: Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks ... |
Title: A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments Abstract: Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented ... |
Title: Self-Supervised Representation Learning via Latent Graph Prediction Abstract: Self-supervised learning (SSL) of graph neural networks is emerging as a promising way of leveraging unlabeled data. Currently, most methods are based on contrastive learning adapted from the image domain, which requires view generatio... |
Title: Task-Agnostic Graph Explanations Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph structured data. Existing learning-based GNN explana... |
Title: Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis Abstract: We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framewor... |
Title: A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines Abstract: Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al. (2019) examined whether deep neural netwo... |
Title: Anomalib: A Deep Learning Library for Anomaly Detection Abstract: This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom ... |
Title: Learning Transferrable Representations of Career Trajectories for Economic Prediction Abstract: Understanding career trajectories -- the sequences of jobs that individuals hold over their working lives -- is important to economists for studying labor markets. In the past, economists have estimated relevant quant... |
Title: The Quarks of Attention Abstract: Attention plays a fundamental role in both natural and artificial intelligence systems. In deep learning, attention-based neural architectures, such as transformer architectures, are widely used to tackle problems in natural language processing and beyond. Here we investigate th... |
Title: Fuzzy Pooling Abstract: Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of th... |
Title: Text-Based Action-Model Acquisition for Planning Abstract: Although there have been approaches that are capable of learning action models from plan traces, there is no work on learning action models from textual observations, which is pervasive and much easier to collect from real-world applications compared to ... |
Title: How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity Abstract: Although traditional optimization methods focus on finding a single optimal solution, most objective functions in modern machine learning problems, especially those in deep learning, often have multiple or infinite numbers... |
Title: Limitations of Neural Collapse for Understanding Generalization in Deep Learning Abstract: The recent work of Papyan, Han, & Donoho (2020) presented an intriguing "Neural Collapse" phenomenon, showing a structural property of interpolating classifiers in the late stage of training. This opened a rich area of exp... |
Title: Generalizable Information Theoretic Causal Representation Abstract: It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems. Existing learning approaches rely on establishing the ... |
Title: Graph Masked Autoencoders with Transformers Abstract: Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenarios due to the fact that deep transformers are hard to train from scratch and th... |
Title: Robust Reinforcement Learning via Genetic Curriculum Abstract: Achieving robust performance is crucial when applying deep reinforcement learning (RL) in safety critical systems. Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert super... |
Title: SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators Abstract: Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy efficiency. However, these emerging devices can suf... |
Title: Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem Abstract: Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due to exponential worst-case complexity. Inspire... |
Title: Federated Stochastic Gradient Descent Begets Self-Induced Momentum Abstract: Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of... |
Title: AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes Abstract: Background: Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patient... |
Title: Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs Abstract: Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they suffer from t... |
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