text stringlengths 0 4.09k |
|---|
Title: Decentralized adaptive clustering of deep nets is beneficial for client collaboration Abstract: We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have ... |
Title: Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast Abstract: A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to ... |
Title: FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning Abstract: Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy due to the requirement of... |
Title: Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis Abstract: Alzheimer's Disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop ... |
Title: Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles Abstract: Trajectory prediction and behavioral decision-making are two important tasks for autonomous vehicles that require good understanding of the environmental context; behavioral decisions are b... |
Title: Truly Unordered Probabilistic Rule Sets for Multi-class Classification Abstract: Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix a... |
Title: Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets Abstract: Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance. Recent studies found that directly training with out-of-distribution data (i.e., open-set samples) in a sem... |
Title: The State of Sparse Training in Deep Reinforcement Learning Abstract: The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision. Their appeal stems largely from the reduced number of parameters required to train and store, as well as in an increase in learning effic... |
Title: Spherical Sliced-Wasserstein Abstract: Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-dimensional projections for which a closed-form solution of the Wasserstein distance is availa... |
Title: Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms Abstract: We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a shareable arm setting, in which several plays can share the same arm. Furthermore, each shareable arm has a finite reward capacity and a ''per-load'' reward d... |
Title: Beyond Ridge Regression for Distribution-Free Data Abstract: In supervised batch learning, the predictive normalized maximum likelihood (pNML) has been proposed as the min-max regret solution for the distribution-free setting, where no distributional assumptions are made on the data. However, the pNML is not def... |
Title: Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay Abstract: We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without the prior k... |
Title: Federated learning with incremental clustering for heterogeneous data Abstract: Federated learning enables different parties to collaboratively build a global model under the orchestration of a server while keeping the training data on clients' devices. However, performance is affected when clients have heteroge... |
Title: Near-Optimal No-Regret Learning for General Convex Games Abstract: A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's \emph{regret} after $T$ repetitions grows polylogarithmically in $T$, an exponential improvement over the tradition... |
Title: Learning Fair Representation via Distributional Contrastive Disentanglement Abstract: Learning fair representation is crucial for achieving fairness or debiasing sensitive information. Most existing works rely on adversarial representation learning to inject some invariance into representation. However, adversar... |
Title: Detecting Adversarial Examples in Batches -- a geometrical approach Abstract: Many deep learning methods have successfully solved complex tasks in computer vision and speech recognition applications. Nonetheless, the robustness of these models has been found to be vulnerable to perturbed inputs or adversarial ex... |
Title: Generalised Policy Improvement with Geometric Policy Composition Abstract: We introduce a method for policy improvement that interpolates between the greedy approach of value-based reinforcement learning (RL) and the full planning approach typical of model-based RL. The new method builds on the concept of a geom... |
Title: Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection Abstract: Code clones are pairs of code snippets that implement similar functionality. Clone detection is a fundamental branch of automatic source code comprehension, having many applications in refactoring recommendati... |
Title: Fast Finite Width Neural Tangent Kernel Abstract: The Neural Tangent Kernel (NTK), defined as $\Theta_\theta^f(x_1, x_2) = \left[\partial f(\theta, x_1)\big/\partial \theta\right] \left[\partial f(\theta, x_2)\big/\partial \theta\right]^T$ where $\left[\partial f(\theta, \cdot)\big/\partial \theta\right]$ is a n... |
Title: Evaluating the Impact of Source Code Parsers on ML4SE Models Abstract: As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern approaches utilize internal code structure in the form of an abstra... |
Title: Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs Abstract: Knowledge bases such as Wikidata amass vast amounts of named entity information, such as multilingual labels, which can be extremely useful for various multilingual and cross-lingual applications. However, such la... |
Title: Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making Abstract: Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task w... |
Title: Maximum Class Separation as Inductive Bias in One Matrix Abstract: Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative ... |
Title: Sheaf Neural Networks with Connection Laplacians Abstract: A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces. SNNs have been shown to have useful theoretical... |
Title: Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning Abstract: Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through reinforcement learning (RL). The difficulty l... |
Title: Sparse Double Descent: Where Network Pruning Aggravates Overfitting Abstract: People usually believe that network pruning not only reduces the computational cost of deep networks, but also prevents overfitting by decreasing model capacity. However, our work surprisingly discovers that network pruning sometimes e... |
Title: BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers Abstract: Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with English usually one of them... |
Title: Understanding Robust Overfitting of Adversarial Training and Beyond Abstract: Robust overfitting widely exists in adversarial training of deep networks. The exact underlying reasons for this are still not completely understood. Here, we explore the causes of robust overfitting by comparing the data distribution ... |
Title: A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications Abstract: The collection of eye gaze information provides a window into many critical aspects of human cognition, health and behaviour. Additionally, many neuroscientific studies complement the behavioural i... |
Title: FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification Abstract: Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) comm... |
Title: The Sensorium competition on predicting large-scale mouse primary visual cortex activity Abstract: The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural ... |
Title: Boosting Factorization Machines via Saliency-Guided Mixup Abstract: Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the paramete... |
Title: Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning Abstract: A digital twin is a surrogate model that has the main feature to mirror the original process behavior. Associating the dynamical process with a digital twin model of reduced complexity has the significant advantage to ... |
Title: Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning Abstract: Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract... |
Title: tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks Abstract: Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher accuracy. However, employing such models on the resource- and energy-constrained embedded platforms is inefficient. Towards this, we present ... |
Title: All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP) Abstract: This paper considers the problem of Hierarchical Multi-Label Classification (HMC), where (i) several labels can be present for each example, and (ii) labels are related via a domain-specific hierarchy tree. Guided... |
Title: Orthonormal Expansions for Translation-Invariant Kernels Abstract: We present a general Fourier analytic technique for constructing orthonormal basis expansions of translation-invariant kernels from orthonormal bases of $\mathscr{L}_2(\mathbb{R})$. This allows us to derive explicit expansions on the real line fo... |
Title: Scalable Differentially Private Clustering via Hierarchically Separated Trees Abstract: We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive with st... |
Title: Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation Abstract: Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image genera... |
Title: RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Abstract: The accelerated proximal point algorithm (APPA), also known as "Catalyst", is a well-established reduction from convex optimization to approximate proximal point computation (i.e., regularized minimization). This reduction is conceptual... |
Title: The Role of Depth, Width, and Activation Complexity in the Number of Linear Regions of Neural Networks Abstract: Many feedforward neural networks generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is an affine function. The nu... |
Title: On Efficient Real-Time Semantic Segmentation: A Survey Abstract: Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. However, many of the top perf... |
Title: On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring Abstract: Gaussian process regression is a powerful method for predicting states based on given data. It has been successfully applied for probabilistic predictions of structural systems to quantify, for example, the crack gr... |
Title: On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models Abstract: A common way to learn and analyze statistical models is to consider operations in the model parameter space. But what happens if we optimize in the parameter space and there is no one-to-one mapping betw... |
Title: Accelerating numerical methods by gradient-based meta-solving Abstract: In science and engineering applications, it is often required to solve similar computational problems repeatedly. In such cases, we can utilize the data from previously solved problem instances to improve the efficiency of finding subsequent... |
Title: Automatic Correction of Human Translations Abstract: We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contr... |
Title: NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning Abstract: Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. However, most of them have two major limitations. Fir... |
Title: DFG-NAS: Deep and Flexible Graph Neural Architecture Search Abstract: Graph neural networks (GNNs) have been intensively applied to various graph-based applications. Despite their success, manually designing the well-behaved GNNs requires immense human expertise. And thus it is inefficient to discover the potent... |
Title: A Flexible Diffusion Model Abstract: Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds. Recently, the deep connection between forward-backward stochastic differential equations (SDEs) and diffusion-based models h... |
Title: Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization Abstract: We focus on the problem of adversarial attacks against models on discrete sequential data in the black-box setting where the attacker aims to craft adversarial examples with limited query ac... |
Title: Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization Abstract: We consider the smooth convex-concave bilinearly-coupled saddle-point problem, $\min_{\mathbf{x}}\max_{\mathbf{y}}~F(\mathbf{x}) + H(\mathbf{x},\mathbf{y}) - G(\mathbf{y})$, where one has access to stochastic first-order oracles f... |
Title: Bootstrapped Transformer for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment. Recent works provide a novel perspective by viewing offline RL as a generic sequence ... |
Title: MET: Masked Encoding for Tabular Data Abstract: We consider the task of self-supervised representation learning (SSL) for tabular data: tabular-SSL. Typical contrastive learning based SSL methods require instance-wise data augmentations which are difficult to design for unstructured tabular data. Existing tabula... |
Title: Boosting Graph Structure Learning with Dummy Nodes Abstract: With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning. However, most of those strategies are designed based on practi... |
Title: How You Start Matters for Generalization Abstract: Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem. In this paper, we promote a shift of focus towards initialization rather than neural architecture or (stochastic) gradient descent to explain t... |
Title: Thompson Sampling for Robust Transfer in Multi-Task Bandits Abstract: We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across mul... |
Title: SOS: Score-based Oversampling for Tabular Data Abstract: Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big succe... |
Title: NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates Abstract: Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates. We introduce NU-Wave 2, a diffusion model for neural au... |
Title: Strategic Representation Abstract: Humans have come to rely on machines for reducing excessive information to manageable representations. But this reliance can be abused -- strategic machines might craft representations that manipulate their users. How can a user make good choices based on strategic representati... |
Title: Large-Margin Representation Learning for Texture Classification Abstract: This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The core of such an approach is a loss function that co... |
Title: Reframed GES with a Neural Conditional Dependence Measure Abstract: In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical representation of the Markov equivalence class (MEC). In this paper,... |
Title: Accelerating Shapley Explanation via Contributive Cooperator Selection Abstract: Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity. To addr... |
Title: SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving Abstract: Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well. Considering the distinctiveness of this community, efficient and re... |
Title: A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models Abstract: Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbations in conditions can often lead to... |
Title: Thompson Sampling Achieves $\tilde O(\sqrt{T})$ Regret in Linear Quadratic Control Abstract: Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on the observed data. In this work, we study... |
Title: MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare Abstract: Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare. In real applications, different federations can seldom wor... |
Title: ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs Abstract: Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from e... |
Title: A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks Abstract: Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a backdoor in the training phase, the adversary could control model predictions via predefined triggers. As various attack and defense models... |
Title: TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction Abstract: Vehicle arrival time prediction has been studied widely. With the emergence of IoT devices and deep learning techniques, estimated time of arrival (ETA) has become a critical component in intelligen... |
Title: What do navigation agents learn about their environment? Abstract: Today's state of the art visual navigation agents typically consist of large deep learning models trained end to end. Such models offer little to no interpretability about the learned skills or the actions of the agent taken in response to its en... |
Title: A Parametric Class of Approximate Gradient Updates for Policy Optimization Abstract: Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e.g. value versus policy representation) or how the learning objective is formulated, yet they shar... |
Title: Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder Abstract: Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesiz... |
Title: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency Abstract: Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short cyclic ... |
Title: TKIL: Tangent Kernel Approach for Class Balanced Incremental Learning Abstract: When learning new tasks in a sequential manner, deep neural networks tend to forget tasks that they previously learned, a phenomenon called catastrophic forgetting. Class incremental learning methods aim to address this problem by ke... |
Title: Revisiting Self-Distillation Abstract: Knowledge distillation is the procedure of transferring "knowledge" from a large model (the teacher) to a more compact one (the student), often being used in the context of model compression. When both models have the same architecture, this procedure is called self-distill... |
Title: Debugging using Orthogonal Gradient Descent Abstract: In this report we consider the following problem: Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch? In other words, can we ``debug" neural networks similar to how we address bugs in ou... |
Title: High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization Abstract: Accurate control of robots in the real world requires a control system that is capable of taking into account the kinodynamic interactions of the robot with its environment. At high speeds, th... |
Title: Multi-Frequency Joint Community Detection and Phase Synchronization Abstract: This paper studies the joint community detection and phase synchronization problem on the \textit{stochastic block model with relative phase}, where each node is associated with a phase. This problem, with a variety of real-world appli... |
Title: Classification of datasets with imputed missing values: does imputation quality matter? Abstract: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed usi... |
Title: Backdoor Attacks on Vision Transformers Abstract: Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a self-attention mechanism that processes images as a sequence of patches, which is quit... |
Title: Zero-Shot AutoML with Pretrained Models Abstract: Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, particularly if D is small? Here, we extend automated machine learning (AutoML) to bes... |
Title: XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence Abstract: Recent advances in machine learning have significantly improved the understanding of source code data and achieved good performance on a number of downstream tasks. Open source repositories like GitHub enable this process with rich unlabel... |
Title: A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features Abstract: Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. However the numerical node feat... |
Title: Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks Abstract: Biclustering on bipartite graphs is an unsupervised learning task that simultaneously clusters the two types of objects in the graph, for example, users and movies in a movie review dataset. The latent block model ... |
Title: PRANC: Pseudo RAndom Networks for Compacting deep models Abstract: Communication becomes a bottleneck in various distributed Machine Learning settings. Here, we propose a novel training framework that leads to highly efficient communication of models between agents. In short, we train our network to be a linear ... |
Title: Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies Abstract: Human vision involves parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emula... |
Title: TUSK: Task-Agnostic Unsupervised Keypoints Abstract: Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e.g. elbow, digit, abstract geometric shape) appears only once in an image. This greatly limits their applicability, as each instance must be isol... |
Title: Local overlap reduction procedure for dynamic ensemble selection Abstract: Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach among the ensemble-based methods in the context of imbalan... |
Title: Quantifying Feature Contributions to Overall Disparity Using Information Theory Abstract: When a machine-learning algorithm makes biased decisions, it can be helpful to understand the sources of disparity to explain why the bias exists. Towards this, we examine the problem of quantifying the contribution of each... |
Title: GOOD: A Graph Out-of-Distribution Benchmark Abstract: Out-of-distribution (OOD) learning deals with scenarios in which training and test data follow different distributions. Although general OOD problems have been intensively studied in machine learning, graph OOD is only an emerging area of research. Currently,... |
Title: I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences Abstract: Machine Learning-as-a-Service (MLaaS) has become a widespread paradigm, making even the most complex machine learning models available for clients via e.g. a pay-per-query principle. This allows users to avoi... |
Title: Active Fairness Auditing Abstract: The fast spreading adoption of machine learning (ML) by companies across industries poses significant regulatory challenges. One such challenge is scalability: how can regulatory bodies efficiently audit these ML models, ensuring that they are fair? In this paper, we initiate t... |
Title: Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data Abstract: Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which ma... |
Title: Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning Abstract: In model-based reinforcement learning, an agent can leverage a learned model to improve its way of behaving in different ways. Two prevalent approaches are decision-time planning and background planning. In this s... |
Title: OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology Abstract: Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts acces... |
Title: SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks Abstract: The core of everyday tasks like reading and driving is active object recognition. Attempts to model such tasks are currently stymied by the inability to incorporate time. People show a flexibl... |
Title: Learning to Teach Fairness-aware Deep Multi-task Learning Abstract: Fairness-aware learning mainly focuses on single task learning (STL). The fairness implications of multi-task learning (MTL) have only recently been considered and a seminal approach has been proposed that considers the fairness-accuracy trade-o... |
Title: Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks Abstract: Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significa... |
Title: Powershap: A Power-full Shapley Feature Selection Method Abstract: Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive... |
Title: SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation Abstract: Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, however, fall short of capturing the mutable natur... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.