Search Results for author:
Found 7 papers, 2 papers with code
Score-Based Multimodal Autoencoders
Multimodal Variational Autoencoders (VAEs) represent a promising group of generative models that facilitate the construction of a tractable posterior within the latent space, given multiple modalities.
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models
To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i. e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR).
Differential Equation Units: Learning Functional Forms of Activation Functions from Data
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure.
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure.
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
In this paper, we instead use efficient truncated randomized search in this reward function to train structured prediction energy networks (SPENs), which provide efficient test-time inference using gradient-based search on a smooth, learned representation of the score landscape, and have previously yielded state-of-the-art results in structured prediction.
Training Structured Prediction Energy Networks with Indirect Supervision
This paper introduces rank-based training of structured prediction energy networks (SPENs).
The Libra Toolkit for Probabilistic Models
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, and sum-product networks.
