18 citations · 41 across the 10 of their papers we have counts for
10 papers
ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
Saiyue Lyu, Zhitian Zhang, Ruizhi Deng +1
We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertain…
An Attention-based Model for Robust Forecasting with Missing Modality
Zhitian Zhang, Wenjie Zi, Yunduz Rakhmangulova +3
Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data. A…
FairNVT: Fair Classification via Noise Injection in Vision Transformers
Qiaoyue Tang, Sepidehsadat Hosseini, Mengyao Zhai +2
This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. FairNV…
LAST SToP For Modeling Asynchronous Time Series
Shubham Gupta, Thibaut Durand, Graham Taylor +1
We present a novel prompt design for Large Language Models (LLMs) tailored to Asynchronous Time Series. Unlike regular time series, which assume values at evenly spaced time points…
Training a Vision Transformer from scratch in less than 24 hours with 1 GPU
Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova +2
Transformers have become central to recent advances in computer vision. However, training a vision Transformer (ViT) model from scratch can be resource intensive and time consuming…
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Yu Gong, Hossein Hajimirsadeghi, Jiawei He +2
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missin…
Point Process Flows
Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed +5
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly model…
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
Akash Abdu Jyothi, Thibaut Durand, Jiawei He +2
Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignor…
A Variational Auto-Encoder Model for Stochastic Point Processes
Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand +3
We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the…
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Thibaut Durand, Nazanin Mehrasa, Greg Mori
Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pi…