For Open-domain QA we report test numbers using 15 retrieved documents for RAG-Token models. Thorough Decoding approach since answers are generally short. Decoding approach for RAG-Sequence models, as Thorough Decoding did not improve performance. Figure 4 shows the user interface for human evaluation. Annotators were encouraged to research the topic using the internet, and were given detailed instructions and worked examples in a full instructions tab.

However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures.

NeurIPS Rebuttal for "Retrieval-Augmented Generation for Knowledge-Intensive NLP T asks" We thank reviewers for their thoughtful, detailed reviews. "information retrieval strategy to improve the the generation Pre-trained seq2seq models have only become available in the last year (T5, BART) or two (GPT2). We study two RAG models. RAG-Sequence's formulation is similar to REALM, but RAG-Token is novel and Further, we explore novel decoding strategies for these models. "contribution [...] is not very specific, since R1 suggested that "A figure or example about P AG-Sequence Model and P AG-Token Model is needed", and R3 mentions "description of the model is quite concise (due to space restrictions)".

The era of big data opens a new opportunity to study input-driven approaches. We introduce a tool that computes the likelihood of patches, given a large dataset to learn from. Given this tool, we present a model that supports the approach and explains lightness and color visual illusions in a unified manner.

Unsupervised text attribute transfer automatically transforms a text to alter a specific attribute (e.g.

The coding rate can be accurately computed from finite samples of degenerate subspace-like distributions and can learn intrinsic representations in supervised, self-supervised, and unsupervised settings in a unified manner.

Neural Information Processing Systems http://nips.cc/

The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative training process to run for numerous steps to convergence. Traditional tuning algorithms only consider the final performance of hyperparam-eters acquired after many expensive iterations and ignore intermediate information from earlier training steps. In this paper, we present a Bayesian optimization (BO) approach which exploits the iterative structure of learning algorithms for efficient hyperparameter tuning. We propose to learn an evaluation function compressing learning progress at any stage of the training process into a single numeric score according to both training success and stability. Our BO framework is then balancing the benefit of assessing a hyperparameter setting over additional training steps against their computation cost. We further increase model efficiency by selectively including scores from different training steps for any evaluated hyper-parameter set. We demonstrate the efficiency of our algorithm by tuning hyperpa-rameters for the training of deep reinforcement learning agents and convolutional neural networks. Our algorithm outperforms all existing baselines in identifying optimal hyperparameters in minimal time.

Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others.