Hey everyone! I'm working on a project with a hotel that wants to use their data for pricing optimization, and I was curious to know what other ML engineers would do in this case. Let's say the hotel gives us all the data about: their competitors prices their favorite market placement within those competitors all nearby and internal events, including holidays (and their importance ranking from 0-100) weather historic internal data (sales, revenue) (own hotel for past 3 years) historic prices (own hotel and all competitors for past 3 years) metasearch data and reviews All this data has 2 col ..read more

My friend implemented the method of Multihead Mixture of Experts in this arxiv paper https://arxiv.org/pdf/2404.15045 and he wanted me to share it with you! Try it out. Let me know what you think and I will pass it on to him. submitted by /u/Prudent_Student2839 [visit reddit] [comments ..read more

Hello guys, I've been working on a sequential labelling using DNA sequences as inputs. Lately there have been 2 foundation models released HyenaDNA (Based on Hyena operator) and Caduceus (based on mamba), I used both pretrained and from scratch models and performances are terrible even with pretrained ones. Does anyone have experience with this type of models, and what are the potential causes for performance drop ? I am literally getting zero performance for the minority class ? Does mamba deal poorly with class imbalance ? submitted by /u/blooming17 [visit reddit] [comments ..read more

I have a use case to use function calling within my application, I am confused whether to choose OpenAI function calling or use Bedrock Agents coupled with Lambda functions for this, which is the best approach? Or help me to choose between these two. submitted by /u/raman_boom [visit reddit] [comments ..read more

ML is very good at solving a niche set of problems, but most of the technical nuances are lost on tech bros and managers. What are some problems you have been told to solve which would be impossible (no data, useless data, unrealistic expectations) or a misapplication of ML (can you have this LLM do all of out accounting). submitted by /u/LanchestersLaw [visit reddit] [comments ..read more

Does anyone know what datasets are out there for causal inference? I’d like to explore methods in the doubly robust ML literature, and I’d like to compensate my learning by working on some datasets and learn the econML software. Does anyone know of any datasets, specifically in the context of marketing/pricing/advertising that would be good sources to apply causal inference techniques? I’m open to other datasets as well. submitted by /u/Direct-Touch469 [visit reddit] [comments ..read more

Dreambooth the MusicGen model suite on small consumer GPUs, in a matter of minutes, using this repository: https://github.com/ylacombe/musicgen-dreamboothing The aim of this project is to provide tools to easily fine-tune and dreambooth the MusicGen model suite, with little data and to leverage a series of optimizations and tricks to reduce resource consumption, thanks to LoRA adaptors. For example, the model can be fine-tuned on a particular music genre or artist to give a checkpoint that generates in that given style. The aim is also to easily share and build on these trained checkpoints, S ..read more

Hello fellow smart people on Reddit, recently I've been thinking about changing my job (I'm a Vision Engineer) and I stumbled upon this position on LinkedIn called Operations Research Scientist. I was wondering after a few years (maybe at most 2) of working in that position, will it be easier for me to transition to a Machine Learning/Artificial Intelligence Engineer or maybe a Computer Vision Engineer role? submitted by /u/unsuccessful_boy [visit reddit] [comments ..read more

Paper: https://arxiv.org/abs/2404.08698 Abstract: While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by allowing the simultaneous generation of multiple tokens. ANPD incorporates a two-stage approach: it begins with a rapid drafting phase that employs an N-gram module, which adapts based on the current interactive context, followed b ..read more

I just wanted to remind or introduce newcomers to this paper. I think this discussion should be re-opened since many people here actually do influence the trends of the field. https://arxiv.org/pdf/1807.03341 On a personal note (feel free to skip): Specifically, I want to point out the issue of "Mathiness", as it seems like this problem got way out of hand and most best papers of conferences suffer from it (one of the most important ML papers tried to be mathy and introduced a big mistake, I believe other papers have bigger issues but no one bothers to check it). So here are my personal point ..read more