text stringlengths 0 4.09k |
|---|
Title: A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis Abstract: Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades co... |
Title: ComPhy: Compositional Physical Reasoning of Objects and Events from Videos Abstract: Objects' motions in nature are governed by complex interactions and their properties. While some properties, such as shape and material, can be identified via the object's visual appearances, others like mass and electric charge... |
Title: Ensemble pruning via an integer programming approach with diversity constraints Abstract: Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classifier... |
Title: Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages Abstract: We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervise... |
Title: OPT: Open Pre-trained Transformer Language Models Abstract: Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant cap... |
Title: A Change Dynamic Model for the Online Detection of Gradual Change Abstract: Changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in process behavior. In cases where these transitions occur g... |
Title: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks Abstract: Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its flexibility and ease of incorpora... |
Title: A Survey on Uncertainty Toolkits for Deep Learning Abstract: The success of deep learning (DL) fostered the creation of unifying frameworks such as tensorflow or pytorch as much as it was driven by their creation in return. Having common building blocks facilitates the exchange of, e.g., models or concepts and m... |
Title: A walk through of time series analysis on quantum computers Abstract: Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict the Fourier coefficie... |
Title: A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits Abstract: The stochastic $K$-armed bandit problem has been studied extensively due to its applications in various domains ranging from online advertising to clinical trials. In practice however, the number of arms can be very large resulting in lar... |
Title: BERTops: Studying BERT Representations under a Topological Lens Abstract: Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT can be a challenging task. In this work, we explore a new di... |
Title: CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning Abstract: In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding invariances from raw pi... |
Title: Understanding CNNs from excitations Abstract: For instance-level explanation, in order to reveal the relations between high-level semantics and detailed spatial information, this paper proposes a novel cognitive approach to neural networks, which named PANE. Under the guidance of PANE, a novel saliency map repre... |
Title: Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters Abstract: Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversar... |
Title: FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation Abstract: Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the majority of existing GCL methods elabo... |
Title: Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion Abstract: Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph co... |
Title: Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning Abstract: We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim ... |
Title: Modeling and mitigation of occupational safety risks in dynamic industrial environments Abstract: Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and rei... |
Title: WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models Abstract: WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined ... |
Title: Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation Abstract: Convolutional neural network (CNN) based image enhancement methods such as super-resolution and detail enhancement have achieved remarkable performances. However, amounts of operations including ... |
Title: Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access Abstract: Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at th... |
Title: Gradient Descent, Stochastic Optimization, and Other Tales Abstract: The goal of this paper is to debunk and dispel the magic behind black-box optimizers and stochastic optimizers. It aims to build a solid foundation on how and why the techniques work. This manuscript crystallizes this knowledge by deriving from... |
Title: Exploration in Deep Reinforcement Learning: A Survey Abstract: This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find th... |
Title: Deep-Attack over the Deep Reinforcement Learning Abstract: Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack. Some work tries to design an attack evalu... |
Title: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs Abstract: Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. To address this problem, it has been recentl... |
Title: Efficient Accelerator for Dilated and Transposed Convolution with Decomposition Abstract: Hardware acceleration for dilated and transposed convolution enables real time execution of related tasks like segmentation, but current designs are specific for these convolutional types or suffer from complex control for ... |
Title: Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model Abstract: Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase... |
Title: Pre-RTL DNN Hardware Evaluator With Fused Layer Support Abstract: With the popularity of the deep neural network (DNN), hardware accelerators are demanded for real time execution. However, lengthy design process and fast evolving DNN models make hardware evaluation hard to meet the time to market need. This pape... |
Title: A Real Time 1280x720 Object Detection Chip With 585MB/s Memory Traffic Abstract: Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints, this paper proposes a low memory traffic DLA chip ... |
Title: PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting Abstract: Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of smal... |
Title: RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds Abstract: Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of differen... |
Title: Zebra: Memory Bandwidth Reduction for CNN Accelerators With Zero Block Regularization of Activation Maps Abstract: The large amount of memory bandwidth between local buffer and external DRAM has become the speedup bottleneck of CNN hardware accelerators, especially for activation maps. To reduce memory bandwidth... |
Title: Sparse Compressed Spiking Neural Network Accelerator for Object Detection Abstract: Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. Howeve... |
Title: BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention Abstract: Increasingly, convolution neural network (CNN) based super resolution models have been proposed for better reconstruction results, but their large model size and complicated structure inhibit their real-time hardware... |
Title: Large Neighborhood Search based on Neural Construction Heuristics Abstract: We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural net... |
Title: Data-driven emotional body language generation for social robotics Abstract: In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to ... |
Title: VICE: Variational Interpretable Concept Embeddings Abstract: A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding objec... |
Title: Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor Abstract: In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hi... |
Title: FedDKD: Federated Learning with Decentralized Knowledge Distillation Abstract: The performance of federated learning in neural networks is generally influenced by the heterogeneity of the data distribution. For a well-performing global model, taking a weighted average of the local models, as done by most existin... |
Title: DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data Abstract: Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic... |
Title: Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN Abstract: Nosie is an important cause of low quality Optical coherence tomography (OCT) image. The neural network model based on Convolutional neural networks(CNNs) has demonstrated its excellent performance in image denoisi... |
Title: A Multi-stage deep architecture for summary generation of soccer videos Abstract: Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive p... |
Title: From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model Abstract: Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused ... |
Title: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study) Abstract: Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling... |
Title: Skeptical binary inferences in multi-label problems with sets of probabilities Abstract: In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider a set of... |
Title: The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks Abstract: The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or eve... |
Title: PSI Draft Specification Abstract: This document presents the draft specification for delivering machine learning services over HTTP, developed as part of the Protocols and Structures for Inference project, which concluded in 2013. It presents the motivation for providing machine learning as a service, followed b... |
Title: Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization Abstract: Networks provide a powerful tool to model complex systems where the different entities in the system are presented by nodes and their interactions by edges. Recently, there has been a growing interest in ... |
Title: LoopStack: a Lightweight Tensor Algebra Compiler Stack Abstract: We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural networks and generate code targetin... |
Title: Physics-aware Reduced-order Modeling of Transonic Flow via $β$-Variational Autoencoder Abstract: Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features. However, two critical drawbacks severely undermine its scala... |
Title: Forecasting Market Changes using Variational Inference Abstract: Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well... |
Title: Attention-wise masked graph contrastive learning for predicting molecular property Abstract: Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improv... |
Title: Using a novel fractional-order gradient method for CNN back-propagation Abstract: Computer-aided diagnosis tools have experienced rapid growth and development in recent years. Among all, deep learning is the most sophisticated and popular tool. In this paper, researchers propose a novel deep learning model and a... |
Title: Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning Abstract: Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications. I... |
Title: Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials Abstract: Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematica... |
Title: Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning Abstract: Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the task is to identify the matching lowest-level ... |
Title: Experimental quantum pattern recognition in IBMQ and diamond NVs Abstract: One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noi... |
Title: Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks Abstract: Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from access... |
Title: Can Information Behaviour Inform Machine Learning? Abstract: The objective of this paper is to explore the opportunities for human information behaviour research to inform and influence the field of machine learning and the resulting machine information behaviour. Using the development of foundation models in ma... |
Title: Generalized Reference Kernel for One-class Classification Abstract: In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities to approximate kernels, r... |
Title: Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection Abstract: While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to unders... |
Title: Deep Learning with Logical Constraints Abstract: In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the backgr... |
Title: Conditional $β$-VAE for De Novo Molecular Generation Abstract: Deep learning has significantly advanced and accelerated de novo molecular generation. Generative networks, namely Variational Autoencoders (VAEs) can not only randomly generate new molecules, but also alter molecular structures to optimize specific ... |
Title: Accurate non-stationary short-term traffic flow prediction method Abstract: Precise and timely traffic flow prediction plays a critical role in developing intelligent transportation systems and has attracted considerable attention in recent decades. Despite the significant progress in this area brought by deep l... |
Title: An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture Abstract: Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a gener... |
Title: Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition Abstract: Video-based action recognition is one of the most popular topics in computer vision. With recent advances of selfsupervised video representation learning approaches, action recognition usually follows a two-... |
Title: Domain Adaptation meets Individual Fairness. And they get along Abstract: Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we leverage this con... |
Title: Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation Abstract: Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how t... |
Title: On the speed of uniform convergence in Mercer's theorem Abstract: The classical Mercer's theorem claims that a continuous positive definite kernel $K({\mathbf x}, {\mathbf y})$ on a compact set can be represented as $\sum_{i=1}^\infty \lambda_i\phi_i({\mathbf x})\phi_i({\mathbf y})$ where $\{(\lambda_i,\phi_i)\}... |
Title: Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs Abstract: Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a powerful formalism capable of d... |
Title: Ridgeless Regression with Random Features Abstract: Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stoch... |
Title: None Class Ranking Loss for Document-Level Relation Extraction Abstract: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express an... |
Title: A Survey of Decentralized Online Learning Abstract: Decentralized online learning (DOL) has been increasingly researched in the last decade, mostly motivated by its wide applications in sensor networks, commercial buildings, robotics (e.g., decentralized target tracking and formation control), smart grids, deep ... |
Title: Reward Systems for Trustworthy Medical Federated Learning Abstract: Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the... |
Title: Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation Abstract: Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiabili... |
Title: An Analysis of the Features Considerable for NFT Recommendations Abstract: This research explores the methods that NFTs can be recommended to people who interact with NFT-marketplaces to explore NFTs of preference and similarity to what they have been searching for. While exploring past methods that can be adopt... |
Title: Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks Abstract: Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present... |
Title: Adaptive Online Optimization with Predictions: Static and Dynamic Environments Abstract: In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this paper, we propose new step... |
Title: Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation? Abstract: This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential ... |
Title: TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources Abstract: Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of ... |
Title: Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize Abstract: This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive ... |
Title: Uniform Manifold Approximation with Two-phase Optimization Abstract: We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is di... |
Title: Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions Abstract: In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instructions crafted by dataset creators. In this ... |
Title: DDDM: a Brain-Inspired Framework for Robust Classification Abstract: Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnera... |
Title: A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness Abstract: Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular appr... |
Title: Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching Abstract: Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetchin... |
Title: A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction Abstract: More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are know... |
Title: Processing Network Controls via Deep Reinforcement Learning Abstract: Novel advanced policy gradient (APG) algorithms, such as proximal policy optimization (PPO), trust region policy optimization, and their variations, have become the dominant reinforcement learning (RL) algorithms because of their ease of imple... |
Title: Neural Network Optimal Feedback Control with Guaranteed Local Stability Abstract: Recent research shows that deep learning can be an effective tool for designing optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of these neural network (NN) controllers is still not wel... |
Title: Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting Abstract: There exists a phenomenon that subjectivity highly lies in the daily evaluation process. Our research primarily concentrates on a multi-person evaluation system with anomaly detection to minimize the possible inaccuracy that su... |
Title: Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF Abstract: The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have made progress in detecting fake news from text. However, e... |
Title: Fair Feature Subset Selection using Multiobjective Genetic Algorithm Abstract: The feature subset selection problem aims at selecting the relevant subset of features to improve the performance of a Machine Learning (ML) algorithm on training data. Some features in data can be inherently noisy, costly to compute,... |
Title: A Simple Duality Proof for Wasserstein Distributionally Robust Optimization Abstract: We present a short and elementary proof of the duality for Wasserstein distributionally robust optimization, which holds for any arbitrary Kantorovich transport distance, any arbitrary measurable loss function, and any arbitrar... |
Title: Combined Learning of Neural Network Weights for Privacy in Collaborative Tasks Abstract: We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network archi... |
Title: Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Abstract: Influence estimation analyzes how changes to the training data can lead to different model predictions; this analysis can help us better understand these predictions, the models making those predictions, and the da... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.