In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high cost and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, Scientific DOcument Retrieval using Multi-level Aspect-based quEries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research. We developed a benchmark dataset within the field of computer science, consisting of 100 human-authored complex query cases. For each complex query, we assembled a collection of 100 relevant documents and produced annotated relevance scores for ranking them.

Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes.

Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific tasks, they insufficiently adapt to new tasks. In supervised learning, this adaptation problem is addressed by large-scale pre-training followed by fine-tuning to new down-stream tasks. Recently, pre-training on multiple tasks has been gaining traction in RL. However, fine-tuning a pre-trained model often suffers from catastrophic forgetting.

We show how to "compile" human-readable programs into standard decoderonly transformer models. Our compiler, Tracr, generates models with known structure. This structure can be used to design experiments. For example, we use it to study "superposition" in transformers that execute multi-step algorithms. Additionally, the known structure of Tracr-compiled models can serve as ground-truth for evaluating interpretability methods. Commonly, because the "programs" learned by transformers are unknown it is unclear whether an interpretation succeeded. We demonstrate our approach by implementing and examining programs including computing token frequencies, sorting, and parenthesis checking.

This section provides a comprehensive overview of the CSMV dataset. The CSMV dataset comprises micro videos and their corresponding comments, which have been updated from February 2020 to October 2022. This extensive time range allows for the inclusion of a diverse set of content, capturing the evolution of sentiments over the course of more than two years. In total, the CSMV dataset comprises 8,210 micro videos, totaling approximately 68.83 hours of video duration, along with 107,267 related comments. The CSMV dataset defines two distinct types of labels, opinion and emotion, for analyzing the sentiment expressed in the comments towards the micro videos. By leveraging the combination of video and textual content in this dataset, researchers can examine the interaction between language expressions and visual cues in sentiment analysis. To deepen our understanding of the CSMV dataset, we performed an analysis of the distribution of videos and related comments using specific hashtags. As depicted in Figure 1, this distribution exhibits a rich diversity of topics in video content. This diversity has brought rich expression of sentiment in user comments, giving the CSMV dataset an advantage in comprehending the complexity of induced sentiment. Moreover, this diversity expands the application of the dataset for multimodal sentiment analysis tasks.

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

Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari. However, it has been deemed challenging or even infeasible to extend Monte Carlo Tree Search (MCTS) based algorithms to diverse real-world applications, especially when these environments involve complex action spaces and significant simulation costs, or inherent stochasticity. In this work, we introduce LightZero, the first unified benchmark for deploying MCTS/MuZero in general sequential decision scenarios. Specificially, we summarize the most critical challenges in designing a general MCTS-style decision-making solver, then decompose the tightly-coupled algorithm and system design of tree-search RL methods into distinct sub-modules. By incorporating more appropriate exploration and optimization strategies, we can significantly enhance these sub-modules and construct powerful LightZero agents to tackle tasks across a wide range of domains, such as board games, Atari, MuJoCo, MiniGrid and GoBigger. Detailed benchmark results reveal the significant potential of such methods in building scalable and efficient decision intelligence.

Then we move to a different approach in Appendix A.2 for the The work in Föllmer (1986) is based on a finite entropy condition, which we report here as Condition 1. Notice that if Assumption 1 is true, then Condition 1 holds (Föllmer (1986), Thm. The proof can be obtained by adapting the result of Lemma 3.6 of Föllmer & Wakolbinger Theorem 4. Let Q be a finite entropy measure. For the proof, we refer to Theorem 3.14 of Föllmer & Wakolbinger (1986). This assumption is simply the translation of H1 from Millet et al. (1989) to our notation.

We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We prove theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. Additionally, we provide a review of the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and comparing with other widely-used machine learning software frameworks. Finally, we present a logistic regression benchmark on the Criteo Terabyte Click Logs dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results, including those obtained using TensorFlow and scikit-learn.