Search Results for author:
Found 27 papers, 6 papers with code
Manual-PA: Learning 3D Part Assembly from Instruction Diagrams
Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner.
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras
Within the USKT architecture, we also propose a bidirectional reverse state space model.
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence.
EnJa: Ensemble Jailbreak on Large Language Models
As Large Language Models (LLMs) are increasingly being deployed in safety-critical applications, their vulnerability to potential jailbreaks -- malicious prompts that can disable the safety mechanism of LLMs -- has attracted growing research attention.
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain.
Temporally Grounding Instructional Diagrams in Unconstrained Videos
In this paper, we tackle this issue by simultaneously grounding a sequence of step diagrams.
Explainable Image Recognition via Enhanced Slot-attention Based Classifier
A novel loss function specifically for ESCOUTER is designed to fine-tune the model's behavior, enabling it to toggle between positive and negative explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture
Given the increasing complexity of AI applications, traditional spatial architectures frequently fall short.
Graph Unlearning with Efficient Partial Retraining
Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications.
Linear-Time Graph Neural Networks for Scalable Recommendations
In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy.
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs.
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Experimental results demonstrate our approach can achieve up to 13. 4x speedup when compared to previous FPGA-based accelerators for the BERT model.
Learning Thresholds with Latent Values and Censored Feedback
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward $g(\gamma, v)$ depends on the proposed threshold $\gamma$ and latent value $v$ and it can be $only$ achieved if the threshold is lower than or equal to the unknown latent value.
Instruct Me More! Random Prompting for Visual In-Context Learning
Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training.
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy.
End-to-End Beam Retrieval for Multi-Hop Question Answering
This approach models the multi-hop retrieval process in an end-to-end manner by jointly optimizing an encoder and two classification heads across all hops.
Ranked #1 on
Question Answering
on HotpotQA
Energy-Dissipative Evolutionary Deep Operator Neural Networks
For an objective operator G, the Branch net encodes different input functions u at the same number of sensors, and the Trunk net evaluates the output function at any location.
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System
It is composed of a visual servoing module that reaches and grasps assembly parts in an unstructured multi-instance and dynamic environment, an action recognition module that performs human action prediction for implicit communication, and a visual handover module that uses the perceptual understanding of human behaviour to produce an intuitive and efficient collaborative assembly experience.
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world.
SPIDR: SDF-based Neural Point Fields for Illumination and Deformation
To enable more accurate illumination updates after deformation, we use the shadow mapping technique to approximate the light visibility updates caused by geometry editing.
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification
This paper proposes a new dimension reduction framework based on rotated multi-fidelity Gaussian process regression and a Bayesian active learning scheme when the available precise observations are insufficient.
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems
We propose a new multi-resolution autoencoder DeepONet model referred to as MultiAuto-DeepONet to deal with this difficulty with the aid of convolutional autoencoder.
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations
We introduce three different models: continuous time, discrete time and hybrid models.
DiLiGenT102: A Photometric Stereo Benchmark Dataset With Controlled Shape and Material Variation
Evaluating photometric stereo using real-world dataset is important yet difficult.
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition.
EvoGAN: An Evolutionary Computation Assisted GAN
Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.
Analyzing Green View Index and Green View Index best path using Google Street View and deep learning
As an important part of urban landscape research, analyzing and studying street-level greenery can increase the understanding of a city's greenery, contributing to better urban living environment planning and design.
