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Title: Function Regression using Spiking DeepONet Abstract: One of the main broad applications of deep learning is function regression. However, despite their demonstrated accuracy and robustness, modern neural network architectures require heavy computational resources to train. One method to mitigate or even resolve ... |
Title: Towards efficient feature sharing in MIMO architectures Abstract: Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful i... |
Title: The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks Abstract: In laboratory object recognition tasks based on undistorted photographs, both adult humans and Deep Neural Networks (DNNs) perform close to ceiling. Unlike adults', whose ob... |
Title: Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application Abstract: In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately, it has been proved that these alter... |
Title: Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens Abstract: It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variables... |
Title: Swapping Semantic Contents for Mixing Images Abstract: Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Au... |
Title: AutoFedNLP: An efficient FedNLP framework Abstract: Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often require private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, ... |
Title: Task Relabelling for Multi-task Transfer using Successor Features Abstract: Deep Reinforcement Learning has been very successful recently with various works on complex domains. Most works are concerned with learning a single policy that solves the target task, but is fixed in the sense that if the environment ch... |
Title: Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review Abstract: With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein ... |
Title: Bayesian Active Learning with Fully Bayesian Gaussian Processes Abstract: The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can... |
Title: A Proximal Algorithm for Sampling from Non-convex Potentials Abstract: We study sampling problems associated with non-convex potentials that meanwhile lack smoothness. In particular, we consider target distributions that satisfy either logarithmic-Sobolev inequality or Poincar\'e inequality. Rather than smooth, ... |
Title: The Fairness of Credit Scoring Models Abstract: In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the popula... |
Title: How to Guide Adaptive Depth Sampling? Abstract: Recent advances in depth sensing technologies allow fast electronic maneuvering of the laser beam, as opposed to fixed mechanical rotations. This will enable future sensors, in principle, to vary in real-time the sampling pattern. We examine here the abstract probl... |
Title: Test-time Batch Normalization Abstract: Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of alleviating distribution shift in test time, we revisit the batch normalization (BN) in the t... |
Title: Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization Abstract: The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer ... |
Title: Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions Abstract: Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual distr... |
Title: Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments Abstract: Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in ... |
Title: Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? Abstract: Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language models is as predictive of human eye fixatio... |
Title: You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas Abstract: Social chatbots, also known as chit-chat chatbots, evolve rapidly with large pretrained language models. Despite the huge progress, privacy concerns have arisen recently: training data of lar... |
Title: Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization Abstract: There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models i... |
Title: Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting Abstract: Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can as... |
Title: Visualizing and Explaining Language Models Abstract: During the last decade, Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence that was massively changed by the advent of Deep Learning. Regardless of the architecture, the language models of the day need to... |
Title: EXODUS: Stable and Efficient Training of Spiking Neural Networks Abstract: Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however,... |
Title: SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer Abstract: In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to i... |
Title: Explanatory machine learning for sequential human teaching Abstract: The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. L... |
Title: DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain Abstract: Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brai... |
Title: Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors Abstract: Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization co... |
Title: Heterformer: A Transformer Architecture for Node Representation Learning on Heterogeneous Text-Rich Networks Abstract: We study node representation learning on heterogeneous text-rich networks, where nodes and edges are multi-typed and some types of nodes are associated with text information. Although recent stu... |
Title: On the SDEs and Scaling Rules for Adaptive Gradient Algorithms Abstract: Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differential Equation (SDE) has allowed researchers to enjoy the benefits of studying a continuous optimization trajectory while carefully preserving the stochasticity of SGD. ... |
Title: Delator: Automatic Detection of Money Laundering Evidence on Transaction Graphs via Neural Networks Abstract: Money laundering is one of the most relevant criminal activities today, due to its potential to cause massive financial losses to governments, banks, etc. We propose DELATOR, a new CAAT (computer-assiste... |
Title: ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities Abstract: Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that com... |
Title: Seeking entropy: complex behavior from intrinsic motivation to occupy action-state path space Abstract: Intrinsic motivation generates behaviors that do not necessarily lead to immediate reward, but help exploration and learning. Here we show that agents having the sole goal of maximizing occupancy of future act... |
Title: Nothing makes sense in deep learning, except in the light of evolution Abstract: Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the ... |
Title: User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars Abstract: Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, in... |
Title: Classifying Human Activities using Machine Learning and Deep Learning Techniques Abstract: Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartp... |
Title: What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment Abstract: The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention. If, in an A/B test, half of users click (... |
Title: A Review of Safe Reinforcement Learning: Methods, Theory and Applications Abstract: Reinforcement learning (RL) has achieved tremendous success in many complex decision making tasks. When it comes to deploying RL in the real world, safety concerns are usually raised, leading to a growing demand for safe RL algor... |
Title: Towards Understanding Grokking: An Effective Theory of Representation Learning Abstract: We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theory and a macroscopic analysis of phase diagra... |
Title: Diverse super-resolution with pretrained deep hiererarchical VAEs Abstract: Image super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. In this work, we tackle the problem of diverse super-resolution by reusing VD-VAE, a state-of-the art... |
Title: Lossless Acceleration for Seq2seq Generation with Aggressive Decoding Abstract: We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq generation at the cost of quality loss,... |
Title: Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss Abstract: This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment int... |
Title: Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography Abstract: BACKGROUND Careful evaluation of the risk of stent under-expansions before the intervention will aid treatment planning, including the application of a pre-stent plaqu... |
Title: Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings Abstract: Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation pr... |
Title: EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning Abstract: Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. However, the current DTL techniques suffe... |
Title: SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks Abstract: The increased interest in Artificial Intelligence (AI) raised the need for highly optimized and sophisticated AI frameworks. Starting with the Lua-based Torch many frameworks have emerged over time, such as Thean... |
Title: A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities Abstract: Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable eff... |
Title: FIND:Explainable Framework for Meta-learning Abstract: Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fai... |
Title: Learning to Reverse DNNs from AI Programs Automatically Abstract: With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs automatically, we propose NNReverse, the first learning-based method whi... |
Title: A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting Abstract: With the growth of transport modes, high traffic forecasting precision is required in intelligent transportation systems. Most previous works utilize the transformer architecture based on graph neural networks and atten... |
Title: Actively Tracking the Optimal Arm in Non-Stationary Environments with Mandatory Probing Abstract: We study a novel multi-armed bandit (MAB) setting which mandates the agent to probe all the arms periodically in a non-stationary environment. In particular, we develop \texttt{TS-GE} that balances the regret guaran... |
Title: Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI Abstract: In this paper, a nonlinear approach to separate different motion types in video data is proposed. This is particularly relevant in dynamic medical imaging (e.g. PET, MRI... |
Title: Deployment of Energy-Efficient Deep Learning Models on Cortex-M based Microcontrollers using Deep Compression Abstract: Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing techn... |
Title: Diversity vs. Recognizability: Human-like generalization in one-shot generative models Abstract: Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing nove... |
Title: DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain Abstract: The Matrix Decomposition techniques have been a vital computational approach to analyzing the hierarchy of functional connectivity in the human brain. However, there are still four... |
Title: A Dynamic Weighted Tabular Method for Convolutional Neural Networks Abstract: Traditional Machine Learning (ML) models like Support Vector Machine, Random Forest, and Logistic Regression are generally preferred for classification tasks on tabular datasets. Tabular data consists of rows and columns corresponding ... |
Title: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures Abstract: Protein complexes are macromolecules essential to the functioning and well-being of all living organisms. As the structure of a protein complex, in particular its region of interaction between multiple protein subunits (i... |
Title: Modernizing Open-Set Speech Language Identification Abstract: While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when ... |
Title: Multilingual Normalization of Temporal Expressions with Masked Language Models Abstract: The detection and normalization of temporal expressions is an important task and a preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real... |
Title: Tackling Provably Hard Representative Selection via Graph Neural Networks Abstract: Representative selection (RS) is the problem of finding a small subset of exemplars from an unlabeled dataset, and has numerous applications in summarization, active learning, data compression and many other domains. In this pape... |
Title: Prototyping three key properties of specific curiosity in computational reinforcement learning Abstract: Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that would offer important benefit... |
Title: ARLO: A Framework for Automated Reinforcement Learning Abstract: Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by allev... |
Title: Learning Geometrically Disentangled Representations of Protein Folding Simulations Abstract: Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative neural network on a structural ensemble ... |
Title: Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent Abstract: Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation ... |
Title: Learning Dense Reward with Temporal Variant Self-Supervision Abstract: Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informativ... |
Title: Towards Better Understanding Attribution Methods Abstract: Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisi... |
Title: Dynamic Ensemble Selection Using Fuzzy Hyperboxes Abstract: Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to the local distribution of the data.... |
Title: How Useful are Gradients for OOD Detection Really? Abstract: One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will furthe... |
Title: Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark Abstract: The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly... |
Title: A Hybrid Model for Forecasting Short-Term Electricity Demand Abstract: Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid pr... |
Title: E2FL: Equal and Equitable Federated Learning Abstract: Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can g... |
Title: PSO-Convolutional Neural Networks with Heterogeneous Learning Rate Abstract: Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computati... |
Title: Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification Abstract: The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNNs are vulnerable to adversarial attac... |
Title: A Survey on Physiological Signal Based Emotion Recognition Abstract: Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regula... |
Title: Masterful: A Training Platform for Computer Vision Models Abstract: Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform's primary goal is to maximize a trained model's accuracy, whi... |
Title: Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training Abstract: Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investi... |
Title: De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning Abstract: Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery. Current approaches to develop molecules for a target protein are intuition-dri... |
Title: DeepStruct: Pretraining of Language Models for Structure Prediction Abstract: We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agn... |
Title: Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation Abstract: In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited a... |
Title: Nuclear Norm Maximization Based Curiosity-Driven Learning Abstract: To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouragin... |
Title: Scaling Laws and Interpretability of Learning from Repeated Data Abstract: Recent large language models have been trained on vast datasets, but also often on repeated data, either intentionally for the purpose of upweighting higher quality data, or unintentionally because data deduplication is not perfect and th... |
Title: Mapping Emulation for Knowledge Distillation Abstract: This paper formalizes the source-blind knowledge distillation problem that is essential to federated learning. A new geometric perspective is presented to view such a problem as aligning generated distributions between the teacher and student. With its guida... |
Title: LSTM-Based Adaptive Vehicle Position Control for Dynamic Wireless Charging Abstract: Dynamic wireless charging (DWC) is an emerging technology that allows electric vehicles (EVs) to be wirelessly charged while in motion. It is gaining significant momentum as it can potentially address the range limitation issue ... |
Title: Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems Abstract: Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-i... |
Title: eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networks Abstract: Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current citie... |
Title: How to Find Actionable Static Analysis Warnings Abstract: Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should not be ignored, we suggest that analysts ... |
Title: Deeper vs Wider: A Revisit of Transformer Configuration Abstract: Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are typically adopted. For example, we often set the base model with h... |
Title: Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network Abstract: The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such a... |
Title: Visualizing CoAtNet Predictions for Aiding Melanoma Detection Abstract: Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of mali... |
Title: Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring Abstract: Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the ... |
Title: Knowledge Distillation from A Stronger Teacher Abstract: Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better fro... |
Title: Automated machine learning: AI-driven decision making in business analytics Abstract: The realization that AI-driven decision-making is indispensable in todays fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for a... |
Title: Neuroevolutionary Feature Representations for Causal Inference Abstract: Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional av... |
Title: KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification Abstract: This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typically solved by using graph neural networks... |
Title: Non-Autoregressive Neural Machine Translation: A Call for Clarity Abstract: Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token. Consequently, their tra... |
Title: Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait Abstract: Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in b... |
Title: Calibration of Natural Language Understanding Models with Venn--ABERS Predictors Abstract: Transformers, currently the state-of-the-art in natural language understanding (NLU) tasks, are prone to generate uncalibrated predictions or extreme probabilities, making the process of taking different decisions based on... |
Title: Lightweight Human Pose Estimation Using Heatmap-Weighting Loss Abstract: Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computa... |
Title: A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans Abstract: Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination... |
Title: Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction Abstract: Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to K... |
Title: CEP3: Community Event Prediction with Neural Point Process on Graph Abstract: Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in t... |
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