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Title: Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks Abstract: We present a generic and trend-aware curriculum learning approach for graph neural networks. It extends existing approaches by incorporating sample-level loss trends to better discriminate easier from harder sa... |
Title: A graph representation of molecular ensembles for polymer property prediction Abstract: Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening ca... |
Title: Learning to Learn Quantum Turbo Detection Abstract: This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC dec... |
Title: Bagged Polynomial Regression and Neural Networks Abstract: Series and polynomial regression are able to approximate the same function classes as neural networks. However, these methods are rarely used in practice, although they offer more interpretability than neural networks. In this paper, we show that a poten... |
Title: All-Photonic Artificial Neural Network Processor Via Non-linear Optics Abstract: Optics and photonics has recently captured interest as a platform to accelerate linear matrix processing, that has been deemed as a bottleneck in traditional digital electronic architectures. In this paper, we propose an all-photoni... |
Title: Multibit Tries Packet Classification with Deep Reinforcement Learning Abstract: High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a... |
Title: OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval Abstract: Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an alignm... |
Title: Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems Abstract: This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.... |
Title: Universal characteristics of deep neural network loss surfaces from random matrix theory Abstract: This paper considers several aspects of random matrix universality in deep neural networks. Motivated by recent experimental work, we use universal properties of random matrices related to local statistics to deriv... |
Title: AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications Abstract: Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using auto... |
Title: Deep Neural Network Classifier for Multi-dimensional Functional Data Abstract: We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data. Specifically, a deep neural network is trained based on the principle components of the training data which... |
Title: Quantum Transfer Learning for Wi-Fi Sensing Abstract: Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802... |
Title: Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles Abstract: While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power awar... |
Title: Hierarchical Distribution-Aware Testing of Deep Learning Abstract: With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Despite recent efforts made in detectin... |
Title: The Power of Reuse: A Multi-Scale Transformer Model for Structural Dynamic Segmentation in Symbolic Music Generation Abstract: Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. Not only that, t... |
Title: Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging Abstract: The curation of large-scale medical datasets from multiple institutions necessary for training deep learning models is challenged by the difficulty in sharing patient data with privacy-preserving. Fede... |
Title: Strategizing against Learners in Bayesian Games Abstract: We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We consider general Bayesian games, where the payoffs of both the optimizer a... |
Title: Learning Quantum Entanglement Distillation with Noisy Classical Communications Abstract: Quantum networking relies on the management and exploitation of entanglement. Practical sources of entangled qubits are imperfect, producing mixed quantum state with reduced fidelity with respect to ideal Bell pairs. Therefo... |
Title: Disentangling Visual Embeddings for Attributes and Objects Abstract: We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct feat... |
Title: High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages Abstract: Estimating forest aboveground biomass at fine spatial scales has become increasingly important for greenhouse gas estimation, monitoring, and verification efforts to mitigate climate change. Airborne LiD... |
Title: High-dimensional additive Gaussian processes under monotonicity constraints Abstract: We introduce an additive Gaussian process framework accounting for monotonicity constraints and scalable to high dimensions. Our contributions are threefold. First, we show that our framework enables to satisfy the constraints ... |
Title: Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification Abstract: Constrained random test generation is one of the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeatedly exercise the ... |
Title: Do Neural Networks Compress Manifolds Optimally? Abstract: Artificial Neural-Network-based (ANN-based) lossy compressors have recently obtained striking results on several sources. Their success may be ascribed to an ability to identify the structure of low-dimensional manifolds in high-dimensional ambient space... |
Title: Recovering Private Text in Federated Learning of Language Models Abstract: Federated learning allows distributed users to collaboratively train a model while keeping each user's data private. Recently, a growing body of work has demonstrated that an eavesdropping attacker can effectively recover image data from ... |
Title: Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks Abstract: Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most eff... |
Title: An Evaluation Framework for Legal Document Summarization Abstract: A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is important to summarize these documents, and ensure that summaries contain ... |
Title: Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer Abstract: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagno... |
Title: Robust Losses for Learning Value Functions Abstract: Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outliers, both skewing the solution of the objective and resulting in high-magnitude... |
Title: Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay Abstract: Voice assistants record sound and can overhear conversations. Thus, a consent management mechanism is desirable such that users can express their wish to be recorded or not. Consent management can be implemented usin... |
Title: Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation Abstract: Speech dereverberation is an important stage in many speech technology applications. Recent work in this area has been dominated by deep neural network models. Temporal convolutional networks (TCNs) ar... |
Title: A Psychological Theory of Explainability Abstract: The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be do... |
Title: On the Privacy of Decentralized Machine Learning Abstract: In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at circumventing the main limitations of federated learning. We identify the decentralized learning properties ... |
Title: Conditional Visual Servoing for Multi-Step Tasks Abstract: Visual Servoing has been effectively used to move a robot into specific target locations or to track a recorded demonstration. It does not require manual programming, but it is typically limited to settings where one demonstration maps to one environment... |
Title: DNNR: Differential Nearest Neighbors Regression Abstract: K-nearest neighbors (KNN) is one of the earliest and most established algorithms in machine learning. For regression tasks, KNN averages the targets within a neighborhood which poses a number of challenges: the neighborhood definition is crucial for the p... |
Title: Function Regression using Spiking DeepONet Abstract: One of the main broad applications of deep learning is function regression. However, despite their demonstrated accuracy and robustness, modern neural network architectures require heavy computational resources to train. One method to mitigate or even resolve ... |
Title: Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels Abstract: Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational wo... |
Title: How do Variational Autoencoders Learn? Insights from Representational Similarity Abstract: The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully understood. For example, the questions of whe... |
Title: Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data Abstract: Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller ... |
Title: Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data Abstract: Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, the performance of DNN is often challenged by traditional machi... |
Title: A unified framework for dataset shift diagnostics Abstract: Most machine learning (ML) methods assume that the data used in the training phase comes from the distribution of the target population. However, in practice one often faces dataset shift, which, if not properly taken into account, may decrease the pred... |
Title: Finite Element Method-enhanced Neural Network for Forward and Inverse Problems Abstract: We introduce a novel hybrid methodology combining classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from ... |
Title: Global Contentious Politics Database (GLOCON) Annotation Manuals Abstract: The database creation utilized automated text processing tools that detect if a news article contains a protest event, locate protest information within the article, and extract pieces of information regarding the detected protest events.... |
Title: Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations Abstract: Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited... |
Title: Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization Abstract: Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-s... |
Title: Measuring Alignment Bias in Neural Seq2Seq Semantic Parsers Abstract: Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-seque... |
Title: KGNN: Distributed Framework for Graph Neural Knowledge Representation Abstract: Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning methods have achieved considerable performance improvem... |
Title: Adaptive Momentum-Based Policy Gradient with Second-Order Information Abstract: The variance reduced gradient estimators for policy gradient methods has been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance... |
Title: Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification Abstract: We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity. Sp... |
Title: IIsy: Practical In-Network Classification Abstract: The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing requirements, while data volume keeps growing. To win the race, machine learning needs t... |
Title: Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks Abstract: In this paper, we present online algorithm called {\it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays $\{d_t\}_{t=1}^T$ is unknown to the algorithm. At ... |
Title: Hyper-Learning for Gradient-Based Batch Size Adaptation Abstract: Scheduling the batch size to increase is an effective strategy to control gradient noise when training deep neural networks. Current approaches implement scheduling heuristics that neglect structure within the optimization procedure, limiting thei... |
Title: ROP inception: signal estimation with quadratic random sketching Abstract: Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging applications, such as phase retrieval or optical processing units. In... |
Title: Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity Abstract: It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T ... |
Title: Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings Abstract: Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation pr... |
Title: blob loss: instance imbalance aware loss functions for semantic segmentation Abstract: Deep convolutional neural networks have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Sorensen Dice coefficient.... |
Title: Is explainable AI a race against model complexity? Abstract: Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we ex... |
Title: Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research Abstract: We establish a dataset of over $1.6\times10^4$ experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this datase... |
Title: An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios Abstract: Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated d... |
Title: Sharp asymptotics on the compression of two-layer neural networks Abstract: In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gauss... |
Title: User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars Abstract: Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, in... |
Title: Moral reinforcement learning using actual causation Abstract: Reinforcement learning systems will to a greater and greater extent make decisions that significantly impact the well-being of humans, and it is therefore essential that these systems make decisions that conform to our expectations of morally good beh... |
Title: Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search Abstract: Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these mo... |
Title: Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Abstract: This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden node of... |
Title: SKILL: Structured Knowledge Infusion for Large Language Models Abstract: Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge... |
Title: SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation Abstract: We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. Unlike previous works on speech representation learning, which learns multilin... |
Title: Active learning of causal probability trees Abstract: The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They e... |
Title: On the Convergence of Policy in Unregularized Policy Mirror Descent Abstract: In this short note, we give the convergence analysis of the policy in the recent famous policy mirror descent (PMD). We mainly consider the unregularized setting following [11] with generalized Bregman divergence. The difference is tha... |
Title: CellTypeGraph: A New Geometric Computer Vision Benchmark Abstract: Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial ... |
Title: Uncertainty-based Network for Few-shot Image Classification Abstract: The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query ... |
Title: Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires Abstract: Electromigration (EM) is one of the major concerns in the reliability analysis of very large scale integration (VLSI) systems due to the continuous technology scaling. Accurately predicting the time... |
Title: Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting Abstract: Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can as... |
Title: Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation Abstract: Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can observe the target nerve and its surrounding structures, the puncture needle's... |
Title: Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms Abstract: The ever-increasing quantity of multivariate process data is driving a need for skilled engineers to analyze, interpret, and build models from such data. Multivariate data analytics relies heavily o... |
Title: Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space Abstract: General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire pol... |
Title: ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks Abstract: Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-constrained devices. As such, multiplicat... |
Title: Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning Abstract: In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks. Armed with the Luo-Tseng error bound condi... |
Title: Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities Abstract: Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, a... |
Title: Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients Abstract: The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically ... |
Title: Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology Abstract: Accurate predictions of the failure progression of structural materials is critical for preventing failure-induced accidents. Despite considerable mechanics modeling-based efforts, accurat... |
Title: Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey Abstract: State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. A big part of these cos... |
Title: Can We Do Better Than Random Start? The Power of Data Outsourcing Abstract: Many organizations have access to abundant data but lack the computational power to process the data. While they can outsource the computational task to other facilities, there are various constraints on the amount of data that can be sh... |
Title: Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher Abstract: Machine unlearning has become an important field of research due to an increasing focus on addressing the evolving data privacy rules and regulations into the machine learning (ML) applications. It facilitates ... |
Title: Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers Abstract: Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of attention is not well understoo... |
Title: A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization Abstract: Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in signif... |
Title: A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management Abstract: Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating c... |
Title: Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization Abstract: Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proli... |
Title: A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks Abstract: Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization wi... |
Title: Robust Perception Architecture Design for Automotive Cyber-Physical Systems Abstract: In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems re... |
Title: "What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation Abstract: We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-cont... |
Title: HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer Abstract: Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug d... |
Title: POViT: Vision Transformer for Multi-objective Design and Characterization of Nanophotonic Devices Abstract: We solve a fundamental challenge in semiconductor IC design: the fast and accurate characterization of nanoscale photonic devices. Much like the fusion between AI and EDA, many efforts have been made to ap... |
Title: Perfect Spectral Clustering with Discrete Covariates Abstract: Among community detection methods, spectral clustering enjoys two desirable properties: computational efficiency and theoretical guarantees of consistency. Most studies of spectral clustering consider only the edges of a network as input to the algor... |
Title: Shape complexity in cluster analysis Abstract: In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preproces... |
Title: Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes Abstract: In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms... |
Title: DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo Abstract: We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging cus... |
Title: Using Embeddings for Causal Estimation of Peer Influence in Social Networks Abstract: We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbors. A main challenge to such estimation is t... |
Title: On Algebraic Constructions of Neural Networks with Small Weights Abstract: Neural gates compute functions based on weighted sums of the input variables. The expressive power of neural gates (number of distinct functions it can compute) depends on the weight sizes and, in general, large weights (exponential in th... |
Title: Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA Abstract: Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays c... |
Title: Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions Abstract: Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (... |
Title: Partial Product Aware Machine Learning on DNA-Encoded Libraries Abstract: DNA encoded libraries (DELs) are used for rapid large-scale screening of small molecules against a protein target. These combinatorial libraries are built through several cycles of chemistry and DNA ligation, producing large sets of DNA-ta... |
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