Search Results for author:
Found 13 papers, 4 papers with code
Training a Vision Transformer from scratch in less than 24 hours with 1 GPU
We introduce some algorithmic improvements to enable training a ViT model from scratch with limited hardware (1 GPU) and time (24 hours) resources.
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.
Learning User Representations for Open Vocabulary Image Hashtag Prediction
In this paper, we introduce an open vocabulary model for image hashtag prediction - the task of mapping an image to its accompanying hashtags.
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
Arbitrarily-conditioned Data Imputation
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.
Variational Selective Autoencoder
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
Learning from Partially-Observed Multimodal Data with Variational Autoencoders
Learning from only partially-observed data for imputation has been an active research area.
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
Recently there is an increasing interest in scene generation within the research community.
A Variational Auto-Encoder Model for Stochastic Point Processes
We propose a novel probabilistic generative model for action sequences.
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex.
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Ranked #3 on
Weakly Supervised Object Detection
on MS COCO
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks
In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON).
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking
For ranking, we propose efficient solutions to exactly solve the inference and the loss-augmented problems.
