text stringlengths 0 4.09k |
|---|
Title: Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics Abstract: Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, ... |
Title: Adversarial Contrastive Learning by Permuting Cluster Assignments Abstract: Contrastive learning has gained popularity as an effective self-supervised representation learning technique. Several research directions improve traditional contrastive approaches, e.g., prototypical contrastive methods better capture t... |
Title: Feature anomaly detection system (FADS) for intelligent manufacturing Abstract: Anomaly detection is important for industrial automation and part quality assurance, and while humans can easily detect anomalies in components given a few examples, designing a generic automated system that can perform at human or a... |
Title: TorchSparse: Efficient Point Cloud Inference Engine Abstract: Deep learning on point clouds has received increased attention thanks to its wide applications in AR/VR and autonomous driving. These applications require low latency and high accuracy to provide real-time user experience and ensure user safety. Unlik... |
Title: Learning Future Object Prediction with a Spatiotemporal Detection Transformer Abstract: We explore future object prediction -- a challenging problem where all objects visible in a future video frame are to be predicted. We propose to tackle this problem end-to-end by training a detection transformer to directly ... |
Title: Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning Abstract: Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simul... |
Title: Provably Efficient Kernelized Q-Learning Abstract: We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected by the effective dimension of the data. We incorporate such ideas into the Q-lear... |
Title: A Framework for Interactive Knowledge-Aided Machine Teaching Abstract: Machine Teaching (MT) is an interactive process where humans train a machine learning model by playing the role of a teacher. The process of designing an MT system involves decisions that can impact both efficiency of human teachers and perfo... |
Title: Model-free Learning of Regions of Attraction via Recurrent Sets Abstract: We consider the problem of learning an inner approximation of the region of attraction (ROA) of an asymptotically stable equilibrium point without an explicit model of the dynamics. Rather than leveraging approximate models with bounded un... |
Title: Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning Abstract: Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were p... |
Title: Differentially Private Learning with Margin Guarantees Abstract: We present a series of new differentially private (DP) algorithms with dimension-independent margin guarantees. For the family of linear hypotheses, we give a pure DP learning algorithm that benefits from relative deviation margin guarantees, as we... |
Title: Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework Abstract: The Machine Assisted Generation, Comparison, and Calibration (MAGCC) framework provides machine assistance and automation of re... |
Title: SoftEdge: Regularizing Graph Classification with Random Soft Edges Abstract: Graph data augmentation plays a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning. Due to their effectiveness, simple edge an... |
Title: Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations Abstract: Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management syst... |
Title: STD: A Seasonal-Trend-Dispersion Decomposition of Time Series Abstract: The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing various hidden components such as the trend, seasonal components,... |
Title: Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders Abstract: Albeit of crucial interest for both financial practitioners and researchers, market-implied volatility data of European swaptions often exhibit large portions of missing quotes due to illiquidity of the v... |
Title: ICDBigBird: A Contextual Embedding Model for ICD Code Classification Abstract: The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigni... |
Title: Staying the course: Locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds Abstract: We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an atlas or by the zeros of a smo... |
Title: A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting Abstract: Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to obtain coherent predictions for a large number of correlated time series that are arranged in a pre-specified tree ... |
Title: CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors Abstract: In cities worldwide, cars cause health and traffic problems which could be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For ci... |
Title: Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification Abstract: Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) ar... |
Title: Learning Sequential Latent Variable Models from Multimodal Time Series Data Abstract: Sequential modelling of high-dimensional data is an important problem that appears in many domains including model-based reinforcement learning and dynamics identification for control. Latent variable models applied to sequenti... |
Title: Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language Abstract: Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), a... |
Title: Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction Abstract: Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task. However, these approaches dep... |
Title: Evolution of Transparent Explainable Rule-sets Abstract: Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to in... |
Title: Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering Abstract: Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex questions that require ... |
Title: SCOPE: Safe Exploration for Dynamic Computer Systems Optimization Abstract: Modern computer systems need to execute under strict safety constraints (e.g., a power limit), but doing so often conflicts with their ability to deliver high performance (i.e. minimal latency). Prior work uses machine learning to automa... |
Title: Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification Abstract: Background: Neural networks produce biased classification results due to correlation bias (they learn correlations between their inputs and outputs to classify samples, even when those correlations do not repr... |
Title: Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach Abstract: Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a ... |
Title: Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions Abstract: Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction... |
Title: Analysis of Temporal Difference Learning: Linear System Approach Abstract: The goal of this technical note is to introduce a new finite-time convergence analysis of temporal difference (TD) learning based on stochastic linear system models. TD-learning is a fundamental reinforcement learning (RL) to evaluate a g... |
Title: NLP Based Anomaly Detection for Categorical Time Series Abstract: Identifying anomalies in large multi-dimensional time series is a crucial and difficult task across multiple domains. Few methods exist in the literature that address this task when some of the variables are categorical in nature. We formalize an ... |
Title: Adversarial Estimators Abstract: We develop an asymptotic theory of adversarial estimators ('A-estimators'). They generalize maximum-likelihood-type estimators ('M-estimators') as their objective is maximized by some parameters and minimized by others. This class subsumes the continuous-updating Generalized Meth... |
Title: Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks Abstract: Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples, the labor cost is prohibitive ... |
Title: Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model Abstract: While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers to develop robust AI algo... |
Title: Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language Abstract: This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work... |
Title: Exploring Hidden Semantics in Neural Networks with Symbolic Regression Abstract: Many recent studies focus on developing mechanisms to explain the black-box behaviors of neural networks (NNs). However, little work has been done to extract the potential hidden semantics (mathematical representation) of a neural n... |
Title: Multi-view Information Bottleneck Without Variational Approximation Abstract: By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we extend the information bottleneck principle to a supervise... |
Title: End-to-end symbolic regression with transformers Abstract: Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of num... |
Title: Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization Abstract: Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstruction pe... |
Title: Sharper Utility Bounds for Differentially Private Models Abstract: In this paper, by introducing Generalized Bernstein condition, we propose the first $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$ high probability excess population risk bound for differentially private algorithms under the assumptions $G$-Li... |
Title: Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference Abstract: Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. Howe... |
Title: Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs Abstract: Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoo... |
Title: Depth Pruning with Auxiliary Networks for TinyML Abstract: Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely constrained environments in tinyML. Unfortunately, special hardware requirem... |
Title: Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues Abstract: Ranking responses for a given dialogue context is a popular benchmark in which the setup is to re-rank the ground-truth response over a limited set of $n$ responses, where $n$ is typically 10. The predominance of this se... |
Title: A piece-wise constant approximation for non-conjugate Gaussian Process models Abstract: Gaussian Processes (GPs) are a versatile and popular method in Bayesian Machine Learning. A common modification are Sparse Variational Gaussian Processes (SVGPs) which are well suited to deal with large datasets. While GPs al... |
Title: Lossy compression of matrices by black-box optimisation of mixed-integer non-linear programming Abstract: In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are ... |
Title: FAIR4Cov: Fused Audio Instance and Representation for COVID-19 Detection Abstract: Audio-based classification techniques on body sounds have long been studied to support diagnostic decisions, particularly in pulmonary diseases. In response to the urgency of the COVID-19 pandemic, a growing number of models are d... |
Title: Emergent Communication for Understanding Human Language Evolution: What's Missing? Abstract: Emergent communication protocols among humans and artificial neural network agents do not yet share the same properties and show some critical mismatches in results. We describe three important phenomena with respect to ... |
Title: Spacing Loss for Discovering Novel Categories Abstract: Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD... |
Title: Balancing Expert Utilization in Mixture-of-Experts Layers Embedded in CNNs Abstract: This work addresses the problem of unbalanced expert utilization in sparsely-gated Mixture of Expert (MoE) layers, embedded directly into convolutional neural networks. To enable a stable training process, we present both soft a... |
Title: Federated Learning via Inexact ADMM Abstract: One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full devices participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based al... |
Title: Modelling graph dynamics in fraud detection with "Attention" Abstract: At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs, few... |
Title: MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering Abstract: Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size... |
Title: Log-based Sparse Nonnegative Matrix Factorization for Data Representation Abstract: Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based rep... |
Title: 3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design Abstract: Generative models for structure-based molecular design hold significant promise for drug discovery, with the potential to speed up the hit-to-lead development cycle, while improving the qualit... |
Title: Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention Abstract: Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the attention coefficients... |
Title: Generative De Novo Protein Design with Global Context Abstract: The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on co... |
Title: Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels Abstract: The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown types of attack. While earlier studies have revealed the benefits of ensemble methods, and in particula... |
Title: TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control Abstract: Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, t... |
Title: Universum-inspired Supervised Contrastive Learning Abstract: Mixup is an efficient data augmentation method which generates additional samples through respective convex combinations of original data points and labels. Although being theoretically dependent on data properties, Mixup is reported to perform well as... |
Title: Quantum Semi-Supervised Kernel Learning Abstract: Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset. Thus, ... |
Title: EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths Abstract: A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, ... |
Title: Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding Abstract: International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT... |
Title: E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR Abstract: Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition. A common solution is to segment the audio in advance using a separate voice activit... |
Title: The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models Abstract: Models of human behavior for prediction and collaboration tend to fall into two categories: ones that learn from large amounts of data via imitation learning, and ones that assume human behavior to be noisily-opt... |
Title: Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation Abstract: The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential elect... |
Title: Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network Abstract: Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee j... |
Title: ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer Abstract: The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper. Using models designed with CNN and Transformer networks, we extrac... |
Title: Centralized Adversarial Learning for Robust Deep Hashing Abstract: Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. Recently, it becomes a hot issue to study adversarial examples which poses a security challenge to deep hashing models. However, th... |
Title: On Feature Learning in Neural Networks with Global Convergence Guarantees Abstract: We study the optimization of wide neural networks (NNs) via gradient flow (GF) in setups that allow feature learning while admitting non-asymptotic global convergence guarantees. First, for wide shallow NNs under the mean-field s... |
Title: MNL-Bandits under Inventory and Limited Switches Constraints Abstract: Optimizing the assortment of products to display to customers is a key to increasing revenue for both offline and online retailers. To trade-off between exploring customers' preference and exploiting customers' choices learned from data, in t... |
Title: A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making Abstract: Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of work has advanced our understanding of these systems by provid... |
Title: Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms Abstract: Understanding and predicting highway lane-change maneuvers is essential for driving modeling and its automation. The development of data-based lane-changing decision-making algorithms is nowaday... |
Title: Learning to Scaffold: Optimizing Model Explanations for Teaching Abstract: Modern machine learning models are opaque, and as a result there is a burgeoning academic subfield on methods that explain these models' behavior. However, what is the precise goal of providing such explanations, and how can we demonstrat... |
Title: Reward Reports for Reinforcement Learning Abstract: The desire to build good systems in the face of complex societal effects requires a dynamic approach towards equity and access. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation abou... |
Title: Convergence of the Riemannian Langevin Algorithm Abstract: We study the Riemannian Langevin Algorithm for the problem of sampling from a distribution with density $\nu$ with respect to the natural measure on a manifold with metric $g$. We assume that the target density satisfies a log-Sobolev inequality with res... |
Title: Memory Bounds for Continual Learning Abstract: Continual learning, or lifelong learning, is a formidable current challenge to machine learning. It requires the learner to solve a sequence of $k$ different learning tasks, one after the other, while retaining its aptitude for earlier tasks; the continual learner s... |
Title: Learning for Spatial Branching: An Algorithm Selection Approach Abstract: The use of machine learning techniques to improve the performance of branch-and-bound optimization algorithms is a very active area in the context of mixed integer linear problems, but little has been done for non-linear optimization. To b... |
Title: Federated Learning Enables Big Data for Rare Cancer Boundary Detection Abstract: Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multip... |
Title: How Sampling Impacts the Robustness of Stochastic Neural Networks Abstract: Stochastic neural networks (SNNs) are random functions and predictions are gained by averaging over multiple realizations of this random function. Consequently, an adversarial attack is calculated based on one set of samples and applied ... |
Title: Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios Abstract: The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on ... |
Title: Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity Abstract: Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational compl... |
Title: Compressibility: Power of PCA in Clustering Problems Beyond Dimensionality Reduction Abstract: In this paper we take a step towards understanding the impact of principle component analysis (PCA) in the context of unsupervised clustering beyond a dimensionality reduction tool. We explore another property of PCA i... |
Title: Generative sampling in tractography using autoencoders (GESTA) Abstract: Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true pathways because some white matter bundles ar... |
Title: Comparative Study of Machine Learning Test Case Prioritization for Continuous Integration Testing Abstract: There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highl... |
Title: Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification Abstract: We describe a practically evaluated approach for training visual cattle ID systems for a whole farm requiring only ten minutes of labelling effort. In particular, for the task of automatic identification of individual Holstein-... |
Title: Error-in-variables modelling for operator learning Abstract: Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state ... |
Title: Revealing Occlusions with 4D Neural Fields Abstract: For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimate 4D visual representations from monocular RGB-D, which is able to persist objec... |
Title: SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation Abstract: Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a starting point for building machine learning mo... |
Title: A Tale of Two Models: Constructing Evasive Attacks on Edge Models Abstract: Full-precision deep learning models are typically too large or costly to deploy on edge devices. To accommodate to the limited hardware resources, models are adapted to the edge using various edge-adaptation techniques, such as quantizat... |
Title: Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion Abstract: Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval perfo... |
Title: A Multi-level Alignment Training Scheme for Video-and-Language Grounding Abstract: To solve video-and-language grounding tasks, the key is for the network to understand the connection between the two modalities. For a pair of video and language description, their semantic relation is reflected by their encodings... |
Title: Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models Abstract: Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection. Unsurprisingly, multiple commercial A... |
Title: Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification Abstract: Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjecti... |
Title: Visual Attention Emerges from Recurrent Sparse Reconstruction Abstract: Visual attention helps achieve robust perception under noise, corruption, and distribution shifts in human vision, which are areas where modern neural networks still fall short. We present VARS, Visual Attention from Recurrent Sparse reconst... |
Title: Statistical inference of travelers' route choice preferences with system-level data Abstract: Traditional network models encapsulate travel behavior among all origin-destination pairs based on a simplified and generic utility function. Typically, the utility function consists of travel time solely and its coeffi... |
Title: CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks Abstract: In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language ... |
Title: Spherical Rotation Dimension Reduction with Geometric Loss Functions Abstract: Modern datasets witness high-dimensionality and nontrivial geometries of spaces they live in. It would be helpful in data analysis to reduce the dimensionality while retaining the geometric structure of the dataset. Motivated by this ... |
Title: Discovering Intrinsic Reward with Contrastive Random Walk Abstract: The aim of this paper is to demonstrate the efficacy of using Contrastive Random Walk as a curiosity method to achieve faster convergence to the optimal policy.Contrastive Random Walk defines the transition matrix of a random walk with the help ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.