Recent advances inface forgery techniques produce nearly visually untraceable deepfake videos, which could be leveraged with malicious intentions. As a result, researchers havebeen devoted todeepfakedetection.

However, its effectiveness in improving the AR of SSMs remains unclear.

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously.

Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands?

We define a Gibbs distribution associated with the problem and evaluate its normalization constant -thepartition functionZ. The partition function, and in particular its logarithm divided by the temperature - the free energy -, contains all the information we aim to understand in the problem. By taking the proper derivatives, and possibly add an external field, we can compute relevant macroscopic properties such as: average overlapwiththegroundtruth,averagelossachieved. Indisordered systemwehavetoconsider the additional complication given by the randomness.

We assume the environment modeled as discrete-time factored-action MDP (FA-MDP)M = hS,A,P,R,γi where S is the set of states s, A is the set of vector-represented actionsa = (a1,...,am),P(s0|s,a) = Pr(st+1 = s0|st = s,at = a)isthe transition probability,R(s,a) R is the immediate reward for taking actiona in state s, and γ [0,1) is the discount factor.

From such expensive feedback, we aim to learn a model of the reward that allows standard RL algorithms to achieve high expected returnswith as few expert queries as possible.