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Found 28 papers, 13 papers with code
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
In machine learning, numerous latest advances of solver designs are accomplished in developing neural operators, a kind of mesh-free approximators of the infinite-dimensional operators that map between different parameterization spaces of equation solutions.
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.
Model-based Reinforcement Learning
reinforcement-learning
+2
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.
