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Found 19 papers, 5 papers with code
Multiple Instance Learning by Discriminative Training of Markov Networks
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks.
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel
Many visual recognition problems can be approached by counting instances.
Discovering Human Interactions in Videos with Limited Data Labeling
We present a novel approach for discovering human interactions in videos.
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision
We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure.
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables
This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space.
Learning from Partially-Observed Multimodal Data with Variational Autoencoders
Learning from only partially-observed data for imputation has been an active research area.
Graph Generation with Variational Recurrent Neural Network
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes.
Variational Selective Autoencoder
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
Adapting Grad-CAM for Embedding Networks
Then, we develop an efficient weight-transfer method to explain decisions for any image without back-propagation.
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.
TD-GEN: Graph Generation With Tree Decomposition
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.
Monotonicity as a requirement and as a regularizer: efficient methods and applications
We study the setting where risk minimization is performed over general classes of models and consider two cases where monotonicity is treated as either a requirement to be satisfied everywhere or a useful property.
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity.
Towards Better Selective Classification
We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset.
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.
Latent Bottlenecked Attentive Neural Processes
We demonstrate that LBANPs can trade-off the computational cost and performance according to the number of latent vectors.
Memory Efficient Neural Processes via Constant Memory Attention Block
Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty.
Constant Memory Attention Block
Modern foundation model architectures rely on attention mechanisms to effectively capture context.
Tree Cross Attention
In this work, we propose Tree Cross Attention (TCA) - a module based on Cross Attention that only retrieves information from a logarithmic $\mathcal{O}(\log(N))$ number of tokens for performing inference.
