We consider the framework of non-stationary stochastic optimization [Besbes et al., 2015] with squared error losses and noisy gradient feedback where the dynamic regret of an online learner against a time varying comparator sequence

Averaging Densely (SW AD), to find flat minima.

We evaluated all models on three additional tasks, beyond those presented in the main paper. This is done by training a fully-connected head applied to each frame's The occurrence of state change is then predicted by training a binary linear classifier, using the concatenated representations as input. Discussion of Results The results on the additional downstream tasks are shown in Tab. For this experiment, we first categorize the activities based on the nature of the transition: 1 T1: irreversible interactions, backward transition highly unlikely (e.g., cut vegetables) T2: reversible interactions, backward transition occurs often (e.g., open/close fridge) T3: interactions with no transition direction (e.g., stirring). As expected, RepLAI learns better associations between the audio and visual state changes than A VID.

Our approach reduces the problem into a sequence of plug-in classifier learning tasks.

When facing uncertainty, decision-makers want predictions they can trust.