Motivated by this, we introduce $\beta$-DQN, a simple and efficient exploration method that augments the standard DQN with a behavior function $\beta$.
In this paper, we propose a scheme for exact attention inference acceleration on memory-constrained edge accelerators, by parallelizing the utilization of heterogeneous compute units, i. e., vector processing units and matrix processing units.
In this work, we propose a simple but effective framework (called ResAD) that can be directly applied to detect anomalies in new classes.
We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards.
This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer models in replicating cross-language structural priming, a key indicator of abstract grammatical representations in human language processing.
Due to the scale of LLM, PEFT operations are usually executed in the public environment (e. g., cloud server).
Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks.
In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i. e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge.
We study and improve MCTS in the context where the environment model is given but imperfect.
With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability.