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Found 14 papers, 2 papers with code
Counter-Contrastive Learning for Language GANs
Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language.
Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language
We assume that the interaction can be modeled as a sequence of templated questions and answers, and that there is a large corpus of previous interactions available.
Improved Training of Mixture-of-Experts Language GANs
In this work, we (1) first empirically show that the mixture-of-experts approach is able to enhance the representation capacity of the generator for language GANs and (2) harness the Feature Statistics Alignment (FSA) paradigm to render fine-grained learning signals to advance the generator training.
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems.
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning
Specifically, these methods do not explicitly utilize the relationship between agents and cannot adapt to different sizes of inputs.
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs.
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning
More specifically, we evaluate the effect of an imaginary transition by calculating the change of the loss computed on the real samples when we use the transition to train the action-value and policy functions.
Model-based Reinforcement Learning
reinforcement-learning
+1
Improving Sequence Generative Adversarial Networks with Feature Statistics Alignment
Generative Adversarial Networks (GAN) are facing great challenges in synthesizing sequences of discrete elements, such as mode dropping and unstable training.
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma.
Deep Self-Supervised Representation Learning for Free-Hand Sketch
In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches.
Deep Learning for Free-Hand Sketch: A Survey
Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present.
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo.
Ranked #5 on
Sketch-Based Image Retrieval
on Chairs
A Comprehensive Survey on Cross-modal Retrieval
To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space.
Cross-Modal Learning via Pairwise Constraints
For unsupervised learning, we propose a cross-modal subspace clustering method to learn a common structure for different modalities.
