Articles for category: AI Research

March 19, 2025

ikayaniaamirshahzad@gmail.com

MoonCast: High-Quality Zero-Shot Podcast Generation

arXiv:2503.14345v1 Announce Type: cross Abstract: Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face challenges when extending their capabilities to long, multi-speaker, and spontaneous dialogues, typical of real-world scenarios such as podcasts. These limitations arise from two primary challenges: 1) long speech: podcasts typically span several minutes, exceeding the upper limit of most existing work; 2) spontaneity: podcasts are marked by their spontaneous, oral nature, which sharply contrasts with formal, written contexts; existing works often fall short in capturing this spontaneity. In this paper, we propose MoonCast,

March 19, 2025

ikayaniaamirshahzad@gmail.com

[2308.11256] Efficient Last-iterate Convergence Algorithms in Solving Games

[Submitted on 22 Aug 2023 (v1), last revised 18 Mar 2025 (this version, v2)] View a PDF of the paper titled Efficient Last-iterate Convergence Algorithms in Solving Games, by Linjian Meng and 8 other authors View PDF HTML (experimental) Abstract:To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies reformulate learning an NE of the original EFG as learning the NEs of a sequence of (perturbed) regularized EFGs. Consequently, proving last-iterate convergence in solving the original EFG reduces to proving last-iterate convergence in solving (perturbed) regularized EFGs. However,

March 19, 2025

ikayaniaamirshahzad@gmail.com

[Project] I built an enterprise-scale Retrieval Augmented Generation system for a Fortune 500 company—now I’ve written a book on it!

Hey r/MachineLearning, I spent the past year and a half building a Retrieval Augmented Generation system for a 100-year-old Fortune 500 manufacturing company with over 50,000 employees and 5 million products. The chatbot we ended up deploying answers thousands of queries a day, searches over 50 million records in 10 to 30 seconds, and it’s officially become the go-to tool for our customer support team. I coded the entire pipeline—from chunking our mountain of PDFs to hooking up concurrency for parallel lookups—and tested it until it was stable enough to hand off. Because so much of this work was trial-and-error

March 19, 2025

ikayaniaamirshahzad@gmail.com

Direct profiling of non-adenosines in poly(A) tails of endogenous and therapeutic mRNAs with Ninetails

In vitro transcription and polyadenylation Set of transcripts with well-defined poly(A) tail were prepared in vitro by T7 RNAP (WT, mutant: G47A + 884G47). Templates for IVT were prepared in consecutive PCR reactions. First, transcript body comprising a fragment of Renilla luciferase was amplified from pClneo-NHA plasmid carrying pRL-5BoxB (Renilla luciferase containing five BoxB structures) with primers comprising specific to Renilla sequence and overhang necessary for PCR2 (RLucX_F1/RLucX_R1) with the following primers: RLucA_F1: GCCATCAGATTGTGTTTGTTAGTCGCTATGATTCCGAGAAGCACGCCGAGAAC RLucA_R1: GCTTACGGTTCACTACTCACGACGATGGGACGATGGCCTTGATCTTGTCTTGG RLucB_F1: GCCATCAGATTGTGTTTGTTAGTCGCTGCTTGTCTGGCCTTTCACTACTCCTACG RLucB_R1: GCTTACGGTTCACTACTCACGACGATGGTCGGGCTTGCCTCCCTTAACGAGAG RLucSh_F1: GCCATCAGATTGTGTTTGTTAGTCGCTCTGGAGCCATTCAAGGAGAAG RLucSh_R1: GCTTACGGTTCACTACTCACGACGATGTTACTGCTCGTTCTTCAGCACGCG The purified amplicon was a template for PCR2 where T7 promoter sequence on primer add T7_F2 and a

March 19, 2025

ikayaniaamirshahzad@gmail.com

Evaluating Different Fewshot Description Prompts on GPT-3

Adam Shimi suggested the idea of trying different fewshot prompts on GPT-3, and hopefully observing something that evidenced larger models being able to handle a wider variety of prompting. He also wrote up a bunch of prompts to try on SST. Unfortunately, the results were kinda mixed: the GPT-2 models all did absolutely terrible and their results were basically useless; the performance wasn’t monotonic with model size (1.3B did better than 2.7B, and babbage did better than curie). Also, the variance increased with performance in general. Mean Accuracy Standard Deviation in Accuracy gpt3-ada 51.9 0.0368 gpt3-babbage 69.4 0.0840 gpt3-curie 67.4