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Found 7 papers, 4 papers with code
Online Bayesian Passive-Aggressive Learning
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning.
A Reverse Hierarchy Model for Predicting Eye Fixations
A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT).
Max-margin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
Learning Where to Sample in Structured Prediction
In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty.
Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on
Dialogue Generation
on Amazon-5
World of Bits: An Open-Domain Platform for Web-Based Agents
While simulated game environments have greatly accelerated research in reinforcement learning, existing environments lack the open-domain realism of tasks in computer vision or natural language processing, which operate on artifacts created by humans in natural, organic settings.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates.
