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Found 40 papers, 18 papers with code
Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions
Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand.
R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal Models
Current geometric data generation approaches, which apply preset templates to generate geometric data or use Large Language Models (LLMs) to rephrase questions and answers (Q&A), unavoidably limit data accuracy and diversity.
Measuring Spiritual Values and Bias of Large Language Models
Large language models (LLMs) have become integral tool for users from various backgrounds.
Debiasing Federated Learning with Correlated Client Participation
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice.
SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding
By joint training SW$_\text{CNN}$ and Mamba, the proposed SWIM structure uses both short-term and long-term information and achieves an accuracy of 86. 2%, which reduces the classification errors by a relative 31. 0% compared to the previous state-of-the-art result.
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning
The event camera has demonstrated significant success across a wide range of areas due to its low time latency and high dynamic range.
FODA-PG for Enhanced Medical Imaging Narrative Generation: Adaptive Differentiation of Normal and Abnormal Attributes
Automatic Medical Imaging Narrative generation aims to alleviate the workload of radiologists by producing accurate clinical descriptions directly from radiological images.
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data.
Bayesian WeakS-to-Strong from Text Classification to Generation
Advances in large language models raise the question of how alignment techniques will adapt as models become increasingly complex and humans will only be able to supervise them weakly.
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
To overcome these challenges, we proposed an interpretable multi-stage approach to flood detection, IMAFD has been proposed.
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition
A promising approach is to use the LLM itself as the safeguard.
Mitigating Prior Shape Bias in Point Clouds via Differentiable Center Learning
Experimental results demonstrate that our method enhances the expressive capacity of existing point cloud models and effectively addresses the issue of information leakage.
Towards interpretable-by-design deep learning algorithms
The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data, while taking advantage of existing latent spaces of large neural networks forming so-called Foundation Models (FM).
RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems.
ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition
First, the Adaptive Threshold Learning module generates two thresholds, namely the clean and noisy thresholds, for each category.
Facial Expression Recognition
Facial Expression Recognition (FER)
Inherent Redundancy in Spiking Neural Networks
In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs.
Deep Directly-Trained Spiking Neural Networks for Object Detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.
Channel Modeling for Heterogeneous Vehicular ISAC System with Shared Clusters
In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously.
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency on edge devices.
BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time.
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection.
Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge
In this paper, we present our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes four sub-challenges of Valence-Arousal (VA) Estimation, Expression (Expr) Classification, Action Unit (AU) Detection and Emotional Reaction Intensity (ERI) Estimation.
Koopman neural operator as a mesh-free solver of non-linear partial differential equations
By approximating the Koopman operator, an infinite-dimensional operator governing all possible observations of the dynamic system, to act on the flow mapping of the dynamic system, we can equivalently learn the solution of a non-linear PDE family by solving simple linear prediction problems.
KoopmanLab: machine learning for solving complex physics equations
To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator family, for learning PDEs without analytic solutions or closed forms.
Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels
Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning.
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting
To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems.
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network
Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks.
An Interpretable Deep Semantic Segmentation Method for Earth Observation
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers.
Continuously Controllable Facial Expression Editing in Talking Face Videos
In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously.
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars.
Deep Reinforcement Learning
Model-based Reinforcement Learning
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Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
The proposed framework can be trained end-to-end with the target task-specific loss, where it learns to explore better pathway configurations and exploit the knowledge in pre-trained models for each target datum.
A unified theory of information transfer and causal relation
Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems.
Brain-inspired Multilayer Perceptron with Spiking Neurons
However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism.
TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation
Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms.
From Big to Small: Adaptive Learning to Partial-Set Domains
Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains.
Discrete Time Convolution for Fast Event-Based Stereo
Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics.
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
(2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose.
Deep Spatial Learning with Molecular Vibration
Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules.
