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Found 9 papers, 5 papers with code
Generative Auto-Bidding with Value-Guided Explorations
Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms.
GAS: Generative Auto-bidding with Post-training Search
We use weak-to-strong search alignment by training small critics for different preferences and an MCTS-inspired search to refine the model's output.
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
Therefore, we appeal for more attention to incremental effectiveness on likelihood, i. e., whether a method could always surpass or at least match the performance of likelihood in U-OOD detection.
Cradle: Empowering Foundation Agents Towards General Computer Control
To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i. e., using screenshots as input and keyboard and mouse actions as output.
Improving Unsupervised Hierarchical Representation with Reinforcement Learning
To address this issue we first analyze the shortcomings of existing methods for mitigating the "posterior collapse" from an information theory perspective then highlight the necessity of regularization for explicitly propagating data information to higher-level latent variables while maintaining the dependency between different levels.
HumanMAC: Masked Motion Completion for Human Motion Prediction
In the training stage, we learn a motion diffusion model that generates motions from random noise.
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Second, the constraints for diversity are designed to be task-agnostic, which causes the constraints to not work well.
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).
