Search Results for author:
Found 45 papers, 15 papers with code
A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs).
Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness
Backdoor attacks pose a severe threat to deep neural networks (DNN) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously.
Explicit Solution of Tunable Input-to-State Safe-Based Controller Under High-Relative-Degree Constraints
This paper investigates the safety analysis and verification of nonlinear systems subject to high-relative-degree constraints and unknown disturbance.
Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network
This study proposed the concept of core urban morphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call CoMo.
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities.
DiverseAgentEntropy: Quantifying Black-Box LLM Uncertainty through Diverse Perspectives and Multi-Agent Interaction
In this paper, we propose a novel method, DiverseAgentEntropy, for evaluating a model's uncertainty using multi-agent interaction under the assumption that if a model is certain, it should consistently recall the answer to the original query across a diverse collection of questions about the same original query.
My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis
In this paper, we refine the IEA task to Personalized Implicit Emotion Analysis (PIEA) and introduce the RAPPIE model, a novel framework designed to address the issue of missing user information within this task.
Human Multi-View Synthesis from a Single-View Model:Transferred Body and Face Representations
Specifically, we use a single-view model pretrained on a large-scale human dataset to develop a multi-view body representation, aiming to extend the 2D knowledge of the single-view model to a multi-view diffusion model.
Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas
This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning
The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different feature distributions under the same task without forgetting old ones.
vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving
This study introduces the vTensor, an innovative tensor structure for LLM inference based on GPU virtual memory management (VMM).
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
Recently, the use of large language models (LLMs) for software code generation, e. g., C/C++ and Python, has proven a great success.
Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net
In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion.
MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction
Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data.
Test-Time Training on Graphs with Large Language Models (LLMs)
Graph Neural Networks have demonstrated great success in various fields of multimedia.
BLINK: Multimodal Large Language Models Can See but Not Perceive
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations.
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models
Predictive models often need to work with incomplete information in real-world tasks.
DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model
As the task of 2D-to-3D reconstruction has gained significant attention in various real-world scenarios, it becomes crucial to be able to generate high-quality point clouds.
Integrating 3D City Data through Knowledge Graphs
Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e. g., OpenStreetMap and existing (Geo)KGs (e. g., Wikidata, DBPedia, and GeoNames), and to perform queries combining information from multiple data sources.
Generic Temporal Reasoning with Differential Analysis and Explanation
Temporal reasoning is the task of predicting temporal relations of event pairs.
Hybrid Multimodal Fusion for Humor Detection
Our experiments demonstrate the effectiveness of our proposed model and hybrid fusion strategy on multimodal fusion, and the AUC of our proposed model on the test set is 0. 8972.
The activity-weight duality in feed forward neural networks: The geometric determinants of generalization
The contribution from a given eigen-direction is the product of two geometric factors (determinants): the sharpness of the loss landscape and the standard deviation of the dual weights, which is found to scale with the weight norm of the solution.
Determination of building flood risk maps from LiDAR mobile mapping data
By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent
We conduct extensive studies over 18 state-of-the-art DL models/tasks and demonstrate that DPSGD often converges in cases where SSGD diverges for large learning rates in the large batch setting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.
Fast and Accurate: Video Enhancement using Sparse Depth
This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising.
Falx: Synthesis-Powered Visualization Authoring
Modern visualization tools aim to allow data analysts to easily create exploratory visualizations.
Human-Computer Interaction Programming Languages
Phases of learning dynamics in artificial neural networks: with or without mislabeled data
Without mislabeled data, we find that the SGD learning dynamics transitions from a fast learning phase to a slow exploration phase, which is associated with large changes in order parameters that characterize the alignment of SGD gradients and their mean amplitude.
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?
We conduct extensive studies over 12 state-of-the-art DL models/tasks and demonstrate that DPSGD consistently outperforms SSGD in the large batch setting; and DPSGD converges in cases where SSGD diverges for large learning rates.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet
We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields.
Strongly Correlated Electrons Materials Science
AI-assisted super-resolution cosmological simulations
Cosmological simulations of galaxy formation are limited by finite computational resources.
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation
Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous driving, robotics, and augmented reality, where efficiency is paramount.
Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: a case study of Hurricane Harvey
Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts.
How neural networks find generalizable solutions: Self-tuned annealing in deep learning
Despite the tremendous success of Stochastic Gradient Descent (SGD) algorithm in deep learning, little is known about how SGD finds generalizable solutions in the high-dimensional weight space.
ASV: Accelerated Stereo Vision System
The key to ASV is to exploit unique characteristics inherent to stereo vision, and apply stereo-specific optimizations, both algorithmically and computationally.
The Quijote simulations
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Intensity mapping with neutral hydrogen and the Hidden Valley simulations
This paper introduces the Hidden Valley simulations, a set of trillion-particle N-body simulations in gigaparsec volumes aimed at intensity mapping science.
Cosmology and Nongalactic Astrophysics
Learning to Predict the Cosmological Structure Formation
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
Disconnected Covariance of 2-point Functions in Large-Scale Structure
We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles.
Cosmology and Nongalactic Astrophysics
Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection
Our method relies on following the gradients of forward model and since the standard way to identify halos is non-differentiable and results in a discrete sample of objects, we propose a framework to model the halo position and mass field starting from the non-linear matter field using Neural Networks.
Cosmology and Nongalactic Astrophysics
nbodykit: an open-source, massively parallel toolkit for large-scale structure
The package is extensively documented at http://nbodykit. readthedocs. io, which also includes an interactive set of example recipes for new users to explore.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach
Based on the attention mechanism, AIM can select microblogs with largest attention values for misinformation identification.
Perturbation theory, effective field theory, and oscillations in the power spectrum
Shell crossings lead to non-perturbative effects, and the PT ignorance can be quantified in terms of their ratio, which is also the transfer function squared in the absence of stochasticity.
Cosmology and Nongalactic Astrophysics
