Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the budget of most organizations. As a result, some research directions become the exclusive domain of a few large industrial and even fewer academic actors. To alleviate this disparity, smaller groups may pool their computational resources and run collaborative experiments that benefit all participants. This paradigm, known as grid-or volunteer computing, has seen successful applications in numerous scientific areas. However, using this approach for machine learning is difficult due to high latency, asymmetric bandwidth, and several challenges unique to volunteer computing. In this work, we carefully analyze these constraints and propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost. Finally, we provide a detailed report of successful collaborative language model pretraining with 40 participants.

Off-policy evaluation (OPE) is important for closing the gap between offline training and evaluation of reinforcement learning (RL), by estimating performance and/or rank of target (evaluation) policies using offline trajectories only. It can improve the safety and efficiency of data collection and policy testing procedures in situations where online deployments are expensive, such as healthcare. However, existing OPE methods fall short in estimating human feedback (HF) signals, as HF may be conditioned over multiple underlying factors and is only sparsely available; as opposed to the agent-defined environmental rewards (used in policy optimization), which are usually determined over parametric functions or distributions. Consequently, the nature of HF signals makes extrapolating accurate OPE estimations to be challenging. To resolve this, we introduce an OPE for HF (OPEHF) framework that revives existing OPE methods in order to accurately evaluate the HF signals. Specifically, we develop an immediate human reward (IHR) reconstruction approach, regularized by environmental knowledge distilled in a latent space that captures the underlying dynamics of state transitions as well as issuing HF signals. Our approach has been tested over two real-world experiments, adaptive in-vivo neurostimulation and intelligent tutoring, as well as in a simulation environment (visual Q&A). Results show that our approach significantly improves the performance toward estimating HF signals accurately, compared to directly applying (variants of) existing OPE methods.

There are no explicit equations for the cerebellum traditionally also has access to a desired state ds (in particular, one can consider this a and forward DNI, respectively; L denotes the loss function. In addition, the inverse model of the of a motor area and sensory area, respectively; CB,CF denotes the computation of backward DNI Notation is largely consistent with section 2 of the main text: hm,hs denotes the hidden activity properties of the inverse model of the cerebellum can be set against those of forward DNI (red). Controller Neocortex Main model Cerebellum Synthesiser Forward Model Backward DNIInverse Model Forward DNI be summarised in table S1. In general, the likeness in formulation between DNI and the cerebellar internal model hypothesis can backward DNI where the main model is an motor-associated RNN. In fact, it was recently suggested that the cerebellum out that though the temporal case of forward DNI was not originally considered in [5], there remain learns to mimic the forward computations which then take place in the neocortex.

Overall, our work offers a novel perspective on the cerebellum as a brainneuronal observations while making several testable predictions across multiple mental observations. Moreover, our model also explains recent behavioural and learning while reducing ataxia-like behaviours, consistent with classical experishown to be cerebellar-dependent. In all tasks, we observe that ccRNNs facilitates and cognitive tasks (pattern recognition and caption generation) that have been network (ccRNN) model on a number of sensorimotor (line and digit drawing) tions from a cerebellar module. We test this cortico-cerebellar recurrent neural in which a recurrent cortical network receives online temporal feedback predicdemonstrate the potential of this framework we introduce a systems-level model lum, helps the cerebral cortex solve similar locking problems akin to DNIs.