Search Results for author:
Found 38 papers, 16 papers with code
Toward Rich Video Human-Motion2D Generation
Generating realistic and controllable human motions, particularly those involving rich multi-character interactions, remains a significant challenge due to data scarcity and the complexities of modeling inter-personal dynamics.
VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models
We present a unified framework for solving partial differential equations (PDEs) using video-inpainting diffusion transformer models.
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images.
RiboFlow: Conditional De Novo RNA Sequence-Structure Co-Design via Synergistic Flow Matching
Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions.
Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records
This study presents the first comprehensive analysis of zero-shot forecasting with GPT-based foundational models in EHRs, introducing a novel pipeline that formulates medical concept prediction as a generative modeling task.
Protein Structure Tokenization: Benchmarking and New Recipe
Recent years have witnessed a surge in the development of protein structural tokenization methods, which chunk protein 3D structures into discrete or continuous representations.
AffinityFlow: Guided Flows for Antibody Affinity Maturation
Antibodies are widely used as therapeutics, but their development requires costly affinity maturation, involving iterative mutations to enhance binding affinity. This paper explores a sequence-only scenario for affinity maturation, using solely antibody and antigen sequences.
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving.
Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
Network interference has attracted significant attention in the field of causal inference, encapsulating various sociological behaviors where the treatment assigned to one individual within a network may affect the outcomes of others, such as their neighbors.
Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management.
Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers
Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design.
Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer
In this work, we explored the possibility of using pre-trained Geom-GNNs as transferable and highly effective geometric descriptors for improved generalization.
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization
Our method leverages a set of structural homologous motifs that align with query structural constraints to guide the generative model in inversely optimizing antibodies according to desired design criteria.
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning.
Real-Time Human Action Recognition on Embedded Platforms
With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical.
XNet v2: Fewer Limitations, Better Results and Greater Universality
So far, however, XNet still faces the limitations, including performance degradation when images lack high-frequency (HF) information, underutilization of raw images and insufficient fusion.
DiffusionPDE: Generative PDE-Solving Under Partial Observation
We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models.
GraphStorm: all-in-one graph machine learning framework for industry applications
GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.
A Simple Approach to Differentiable Rendering of SDFs
We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines.
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance. New methods are required to capture the distinctive local context for more robust novel object detection.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
Ranked #1 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+2
Accurate Differential Operators for Hybrid Neural Fields
Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts.
Pure Exploration in Asynchronous Federated Bandits
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server.
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction
Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions.
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters.
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Furthermore, we present a comprehensive analysis to understand the mystery behind prompt robustness and its transferability.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
We propose the first study of adversarial attacks on online learning to rank.
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
There is a broad consensus that news media outlets incorporate ideological biases in their news articles.
Computational Assessment of Hyperpartisanship in News Titles
We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2, 200 manually labeled and 1. 8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner.
Training self-supervised peptide sequence models on artificially chopped proteins
In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
