Theworddistributions of topics, their optimal transports to the word distributions of documents, and the embeddings of words are learned in a unified framework. When learning thetopic model, weleverage adistilled underlying distance matrix toupdate the topic distributions and smoothly calculate the corresponding optimal transports.

We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. The proposed framework is able to learn proof search strategies as well as tactic and arguments prediction in an end-to-end manner. We formulate the process of ITP as a Markov decision process (MDP) in which each state represents a set of potential derivation paths. This structure allows us to introduce a novel backtracking mechanism which enables the agent to efficiently discard (predicted) dead-end derivations and restart the derivation from promising alternatives. We implement the framework in the HOL theorem prover. Experimental results show that the framework using learned search strategies outperforms existing automated theorem provers (i.e., hammers) available in HOL when evaluated on unseen problems. We further elaborate the role of key components of the framework using ablation studies.

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision and the related issues on quality, reliability, diversity, self-consistency, and undesirable biases. To address these challenges, we propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision. Our approach encompasses four stages: first, we use an LLM to generate synthetic prompts, and a topic-guided method to augment the prompt diversity; second, we use a small set of human-written principles for AI models to follow, and guide the LLM through in-context learning from demonstrations (of principles application) to produce helpful, ethical, and reliable responses to user's queries; third, we fine-tune the original LLM with the high-quality self-aligned responses so that the resulting model can generate desirable responses for each query directly without the principle set and the demonstrations anymore; and finally, we offer a refinement step to address the issues of overly-brief or indirect responses. Applying SELF-ALIGN to the LLaMA-65b base language model, we develop an AI assistant named Dromedary. With fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning).

In this article we will learn how to code Point Net from scratch in PyTorch. This version of Point Net will allow for classification or semantic segmentation. If you are not familiar with Point Net please see this article. If you would just like to code it, please proceed forward, we will break down Point Net and try to understand it piece by piece. The code for this article is stored in this repository.

Zyro's Head of AI, Tomas Rasymas told Express.co.uk: "Previously, creating a professional logo was a lengthy and costly process which involved expert help from a designer. "In fact, the average UK small business spends £750 on designing their logo. "But new, experimental AI tools -- like our new logo generator -- will soon make it possible for businesses to create a logo for their brand without the hefty price tag associated with hiring a designer." As part of their ongoing experiments, Zyro's team have been training the AI to draw new logos for some of the world's biggest brands.

Originally developed by me (Nicklas Hansen), Peter E. Christensen and Alexander R. Johansen as educational material for the graduate deep learning course at the Technical University of Denmark (DTU). You can access the full course material here. Inspired by the great Andrej Karpathy. In this lab we will introduce different ways of learning from sequential data. As an example, we will train a neural network to do language modelling, i.e. predict the next token in a sentence.

A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly surveys approximation methods, and closes with a brief discussion of open issues. Often, truth is more elusive, and categorical statements can only be made by judgment of the likelihood or other ordinal attribute of competing propositions. Probability theory is the oldest and best-understood theory for representing and reasoning about such situations, but early AI experimental efforts at applying probability theory were disappointing and only confirmed a belief among AI researchers that those who worried about numbers were "missing the point."