text stringlengths 0 4.09k |
|---|
Title: Generative Modeling Helps Weak Supervision (and Vice Versa) Abstract: Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on gro... |
Title: Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing Abstract: This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane... |
Title: Bioplastic Design using Multitask Deep Neural Networks Abstract: Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as ... |
Title: Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning Abstract: Reinforcement learning has received significant interest in recent years, due primarily to the successes of deep reinforcement learning at solving many challenging tasks such as playing Chess, Go and online ... |
Title: WayFAST: Traversability Predictive Navigation for Field Robots Abstract: We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB ... |
Title: DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification Abstract: Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classificati... |
Title: Deep Portrait Delighting Abstract: We present a deep neural network for removing undesirable shading features from an unconstrained portrait image, recovering the underlying texture. Our training scheme incorporates three regularization strategies: masked loss, to emphasize high-frequency shading features; soft-... |
Title: Toward Physically Realizable Quantum Neural Networks Abstract: There has been significant recent interest in quantum neural networks (QNNs), along with their applications in diverse domains. Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quant... |
Title: Learning curves for the multi-class teacher-student perceptron Abstract: One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with the single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both B... |
Title: Learning by non-interfering feedback chemical signaling in physical networks Abstract: Both non-neural and neural biological systems can learn. So rather than focusing on purely brain-like learning, efforts are underway to study learning in physical systems. Such efforts include equilibrium propagation (EP) and ... |
Title: Fast on-line signature recognition based on VQ with time modeling Abstract: This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used the MCYT database, which consists of 330 users and 25 skilled forgeries per person performed by 5 different impostors. This ... |
Title: An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing Abstract: Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic m... |
Title: An Optical Controlling Environment and Reinforcement Learning Benchmarks Abstract: Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based controllers. The environment incorporates nonconve... |
Title: NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty Abstract: A robust body of reinforcement learning techniques have been developed to solve complex sequential decision making problems. However, these methods assume that train and evaluation tasks come from similarly or identically distribut... |
Title: Matrix Completion with Heterogonous Cost Abstract: The matrix completion problem has been studied broadly under many underlying conditions. The problem has been explored under adaptive or non-adaptive, exact or estimation, single-phase or multi-phase, and many other categories. In most of these cases, the observ... |
Title: Pixel VQ-VAEs for Improved Pixel Art Representation Abstract: Machine learning has had a great deal of success in image processing. However, the focus of this work has largely been on realistic images, ignoring more niche art styles such as pixel art. Additionally, many traditional machine learning models that f... |
Title: Should Machine Learning Models Report to Us When They Are Clueless? Abstract: The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which describes the extent to which AI models can b... |
Title: Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters Abstract: In many applications in statistics and machine learning, the availability of data samples from multiple possibly heterogeneous sources has become increasingly prevalent. On the other hand, in distributionally robust optimizat... |
Title: Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem Abstract: Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development, aiming to formally define probabilistic models, guiding development of objective functions,... |
Title: 3D-EDM: Early Detection Model for 3D-Printer Faults Abstract: With the advent of 3D printers in different price ranges and sizes, they are no longer just for professionals. However, it is still challenging to use a 3D printer perfectly. Especially, in the case of the Fused Deposition Method, it is very difficult... |
Title: Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images Abstract: Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases. However, segmenting the VBs and IVDs simultaneously is not ... |
Title: An Emulation Framework for Fire Front Spread Abstract: Forecasting bushfire spread is an important element in fire prevention and response efforts. Empirical observations of bushfire spread can be used to estimate fire response under certain conditions. These observations form rate-of-spread models, which can be... |
Title: An Adaptive Gradient Method with Energy and Momentum Abstract: We introduce a novel algorithm for gradient-based optimization of stochastic objective functions. The method may be seen as a variant of SGD with momentum equipped with an adaptive learning rate automatically adjusted by an 'energy' variable. The met... |
Title: Learning to Censor by Noisy Sampling Abstract: Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertently, capture sensitive information that can leak semantic and geometric properties of t... |
Title: Biceph-Net: A robust and lightweight framework for the diagnosis of Alzheimer's disease using 2D-MRI scans and deep similarity learning Abstract: Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have be... |
Title: Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging Abstract: Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent d... |
Title: Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably) Abstract: Despite the remarkable success of deep multi-modal learning in practice, it has not been well-explained in theory. Recently, it has been observed that the best uni-modal network outperforms the join... |
Title: Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions Abstract: The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules. In the trending approach of constructive super... |
Title: A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification Abstract: The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model with clusters to jointl... |
Title: New Distinguishers for Negation-Limited Weak Pseudorandom Functions Abstract: We show how to distinguish circuits with $\log k$ negations (a.k.a $k$-monotone functions) from uniformly random functions in $\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random samples. The previous best distingu... |
Title: Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization Abstract: The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node rep... |
Title: PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation Abstract: The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has received increasing attention in recent years. In... |
Title: Efficient Fully Distributed Federated Learning with Adaptive Local Links Abstract: Nowadays, data-driven, machine and deep learning approaches have provided unprecedented performance in various complex tasks, including image classification and object detection, and in a variety of application areas, like autonom... |
Title: Increasing the accuracy and resolution of precipitation forecasts using deep generative models Abstract: Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction mode... |
Title: Input-specific Attention Subnetworks for Adversarial Detection Abstract: Self-attention heads are characteristic of Transformer models and have been well studied for interpretability and pruning. In this work, we demonstrate an altogether different utility of attention heads, namely for adversarial detection. Sp... |
Title: NavDreams: Towards Camera-Only RL Navigation Among Humans Abstract: Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While ima... |
Title: Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices Abstract: In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording. We first construct an ASC baseli... |
Title: The BP Dependency Function: a Generic Measure of Dependence between Random Variables Abstract: Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Typical questions are: `Do underlying relationships exist?', `Are some variables redundant?', and `Is s... |
Title: Towards explaining the generalization gap in neural networks using topological data analysis Abstract: Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topolog... |
Title: Binary Morphological Neural Network Abstract: In the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks. However, they are not necessarily optimal. For example, mathematical morphology is known to be better suited to deal with ... |
Title: MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images Abstract: The lack of annotated datasets is a major challenge in training new task-specific supervised AI algorithms as manual annotation is expensive and time-consuming. To address this problem, we present MONAI Label, a free and... |
Title: Ethereum Fraud Detection with Heterogeneous Graph Neural Networks Abstract: While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine learning techniques detect suspicious transactions that ... |
Title: MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data Abstract: Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically motivated speech perception metrics as part of model tr... |
Title: Dynamically-Scaled Deep Canonical Correlation Analysis Abstract: Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. Several variants of CCA have been introduced in the literature, in particular, variants based on deep n... |
Title: Verification of safety critical control policies using kernel methods Abstract: Hamilton-Jacobi reachability methods for safety-critical control have been well studied, but the safety guarantees derived rely on the accuracy of the numerical computation. Thus, it is crucial to understand and account for any inacc... |
Title: U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search Abstract: Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardwar... |
Title: Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers Abstract: A new privacy-preserving federated learning framework allowing laser manufacturers to collaboratively build a robust ML-based laser lifetime prediction model, is proposed. It achieves a mean absolute error of 0.1 years and a si... |
Title: A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction Abstract: A hybrid prognostic model based on convolutional neural networks (CNN) and long short-term memory (LSTM) is proposed to predict the laser remaining useful life (RUL). The experimental results show that it outperforms the conventional... |
Title: Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing Abstract: Mobile Crowdsensing (MCS) is a sensing paradigm that has transformed the way that various service providers collect, process, and analyze data. MCS offers novel processes where data is sensed and shared throu... |
Title: Deep Multi-View Learning for Tire Recommendation Abstract: We are constantly using recommender systems, often without even noticing. They build a profile of our person in order to recommend the content we will most likely be interested in. The data representing the users, their interactions with the system or th... |
Title: MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels Abstract: The success of deep convolutional neural networks (DCNNs) benefits from high volumes of annotated data. However, annotating medical images is laborious, expensive, and requires human expertise, which induc... |
Title: Reducing overestimating and underestimating volatility via the augmented blending-ARCH model Abstract: SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-G... |
Title: Activation-Based Sampling for Pixel- to Image-Level Aggregation in Weakly-Supervised Segmentation Abstract: Classification networks can be used to localize and segment objects in images by means of class activation maps (CAMs). However, without pixel-level annotations, they are known to (1) mainly focus on discr... |
Title: 3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy Abstract: Planetary exploration depends heavily on 3D image data to characterize the static and dynamic properties of the rock and environment. Analyzing 3D... |
Title: A Deep Learning Framework to Reconstruct Face under Mask Abstract: While deep learning-based image reconstruction methods have shown significant success in removing objects from pictures, they have yet to achieve acceptable results for attributing consistency to gender, ethnicity, expression, and other character... |
Title: Quantum-enhanced Markov chain Monte Carlo Abstract: Sampling from complicated probability distributions is a hard computational problem arising in many fields, including statistical physics, optimization, and machine learning. Quantum computers have recently been used to sample from complicated distributions tha... |
Title: A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset Abstract: Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a ... |
Title: Sampling Theorems for Unsupervised Learning in Linear Inverse Problems Abstract: Solving a linear inverse problem requires knowledge about the underlying signal model. In many applications, this model is a priori unknown and has to be learned from data. However, it is impossible to learn the model using observat... |
Title: Semi-Supervised Graph Learning Meets Dimensionality Reduction Abstract: Semi-supervised learning (SSL) has recently received increased attention from machine learning researchers. By enabling effective propagation of known labels in graph-based deep learning (GDL) algorithms, SSL is poised to become an increasin... |
Title: A Deep Learning Approach to Probabilistic Forecasting of Weather Abstract: We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve... |
Title: Pathways: Asynchronous Distributed Dataflow for ML Abstract: We present the design of a new large scale orchestration layer for accelerators. Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research ideas, while retaining state of the art performance for current models. P... |
Title: Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation Abstract: Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performance. These pairwise constraints, w... |
Title: GriTS: Grid table similarity metric for table structure recognition Abstract: In this paper, we propose a new class of evaluation metric for table structure recognition, grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form as a mat... |
Title: A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks Abstract: Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi... |
Title: Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal Abstract: Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multipl... |
Title: Minimax Regret for Cascading Bandits Abstract: Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting. For the case of unstructured rewards, we prove matching upper and lower bounds for the problem-independent (i.e., gap-free) r... |
Title: TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging Abstract: Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning architectures for sleep staging. However... |
Title: Your Policy Regularizer is Secretly an Adversary Abstract: Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show how this robustness arises from hedging against worst-case perturbations ... |
Title: Deep Learning based Intelligent Coin-tap Test for Defect Recognition Abstract: The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent signal processing method, convolutional neural networks ... |
Title: PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression Abstract: Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status ... |
Title: ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest Abstract: We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the mas... |
Title: Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback Abstract: This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a personalizable deep metric model that captures b... |
Title: R3M: A Universal Visual Representation for Robot Manipulation Abstract: We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a visual representation using the Ego4D human video dataset usi... |
Title: Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory Abstract: Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OT... |
Title: Improving the Fairness of Chest X-ray Classifiers Abstract: Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has found that such classifiers can exhibit biases in the form of gaps in pre... |
Title: AI Poincar\'{e} 2.0: Machine Learning Conservation Laws from Differential Equations Abstract: We present a machine learning algorithm that discovers conservation laws from differential equations, both numerically (parametrized as neural networks) and symbolically, ensuring their functional independence (a non-li... |
Title: Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions Abstract: Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been fully explored for clinical data anal... |
Title: Evaluation of Non-Invasive Thermal Imaging for detection of Viability of Onchocerciasis worms Abstract: Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive ... |
Title: MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion Abstract: Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifact, denoising is largely studied both within the medical imaging community and beyond th... |
Title: Are Evolutionary Algorithms Safe Optimizers? Abstract: We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life. Such problems are referred to as safe optimization ... |
Title: Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization Abstract: We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA). The computational premise of quan... |
Title: Applications of physics informed neural operators Abstract: We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ... |
Title: Asynchronous Collaborative Learning Across Data Silos Abstract: Machine learning algorithms can perform well when trained on large datasets. While large organisations often have considerable data assets, it can be difficult for these assets to be unified in a manner that makes training possible. Data is very oft... |
Title: Linearizing Transformer with Key-Value Memory Abstract: Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer. Among them are low-rank projection methods such as Linformer and kernel-based Transformers. Despite t... |
Title: Computed Tomography Reconstruction using Generative Energy-Based Priors Abstract: In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which ma... |
Title: A Supervised Machine Learning Approach for Sequence Based Protein-protein Interaction (PPI) Prediction Abstract: Computational protein-protein interaction (PPI) prediction techniques can contribute greatly in reducing time, cost and false-positive interactions compared to experimental approaches. Sequence is one... |
Title: Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions Abstract: A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundam... |
Title: Pseudo Label Is Better Than Human Label Abstract: State-of-the-art automatic speech recognition (ASR) systems are trained with tens of thousands of hours of labeled speech data. Human transcription is expensive and time consuming. Factors such as the quality and consistency of the transcription can greatly affec... |
Title: Competency Assessment for Autonomous Agents using Deep Generative Models Abstract: For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards this objective, we develop probabilistic world models... |
Title: A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data Abstract: The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks ... |
Title: Vision-Based Manipulators Need to Also See from Their Hands Abstract: We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-i... |
Title: Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs Abstract: Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting a... |
Title: Possibility Before Utility: Learning And Using Hierarchical Affordances Abstract: Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead o... |
Title: Enhancing Classifier Conservativeness and Robustness by Polynomiality Abstract: We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among oth... |
Title: On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study Abstract: Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attributi... |
Title: The Challenges of Continuous Self-Supervised Learning Abstract: Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to learn representations from data in-the-wild, i.e., without the need for ... |
Title: Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition Abstract: An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes nov... |
Title: Towards Backwards-Compatible Data with Confounded Domain Adaptation Abstract: Most current domain adaptation methods address either covariate shift or label shift, but are not applicable where they occur simultaneously and are confounded with each other. Domain adaptation approaches which do account for such con... |
Title: Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing Abstract: Federated learning is a prime candidate for distributed machine learning at the network edge due to the low communication complexity and privacy protection among other attractive properties. However, existing algorith... |
Title: Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Abstract: Bayesian optimization is a gold standard for query-efficient continuous optimization. However, its adoption for drug and antibody sequence design has been hindered by the discrete, high-dimensional nature of t... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.