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Found 16 papers, 4 papers with code
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.
An Early Evaluation of GPT-4V(ision)
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio.
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators.
Wasserstein Distributional Learning
To overcome this fundamental issue, we propose Wasserstein Distributional Learning (WDL), a flexible density-on-scalar regression modeling framework that starts with the Wasserstein distance $W_2$ as a proper metric for the space of density outcomes.
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference.
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation
Based on INS-Conv, an online joint 3D semantic and instance segmentation pipeline is proposed, reaching an inference speed of 15 FPS on GPU and 10 FPS on CPU.
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond.
Weakly Supervised Learning Creates a Fusion of Modeling Cultures
The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001).
Cultural Vocal Bursts Intensity Prediction
Weakly-supervised Learning
Artificial Perceptual Learning: Image Categorization with Weak Supervision
In this paper, we propose a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised image categorization.
Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions
We compare all models using simulated and real data.
Applications Methodology
Online Estimation and Community Detection of Network Point Processes for Event Streams
To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models.
Next Waves in Veridical Network Embedding
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.
OccuSeg: Occupancy-aware 3D Instance Segmentation
3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days.
Ranked #1 on
3D Instance Segmentation
on SceneNN
Stabilized Sparse Online Learning for Sparse Data
To mitigate this issue, we introduce a stabilized truncated stochastic gradient descent algorithm.
Boosted Sparse Non-linear Distance Metric Learning
In this paper, we propose a nonlinear sparse metric learning algorithm via boosting.
Topic-adjusted visibility metric for scientific articles
In this work, we address the problem of field variation and introduce an article level metric useful for evaluating individual articles' visibility.
