However, the structure of their hidden layer representations is only theoretically well-understood incertain infinite-width limits, inwhichtheserepresentations cannot flexibly adapt tolearn data-dependent features [3-11,24]. Inthe Bayesian setting, these representations are described by fixed, deterministic kernels [3-11].

We show in this work that reinforcement learning can be successfully applied to decoding short to moderate length sparse graph-based channel codes.

Solving complex planning problems has been a long-standing challenge in computer science.

We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility. We propose two provably efficient model-free algorithms, Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ). These algorithms implement a form of risk-sensitive optimism in the face of uncertainty, which adapts to both riskseeking and risk-averse modes of exploration.

However, to obtain the desired effect, the step-size should be chosen sufficiently large, a task which is problem dependent andcanbedifficultinpractice.

To address the label scarcity problem in practice, a number of approaches apply the test-time adaptation setting to the domain of adapting VLMs to downstream tasks, as shown in Figure 1.

Convolutional Neural Networks (CNNs) are computation intensive, making their deployment on resource-constrained devices (e.g., Microcontrollers equipped with 2MB memory) challenging.