Articles for category: AI Research

March 4, 2025

ikayaniaamirshahzad@gmail.com

[2412.17762] The Superposition of Diffusion Models Using the Itô Density Estimator

[Submitted on 23 Dec 2024 (v1), last revised 28 Feb 2025 (this version, v2)] View a PDF of the paper titled The Superposition of Diffusion Models Using the It\^o Density Estimator, by Marta Skreta and Lazar Atanackovic and Avishek Joey Bose and Alexander Tong and Kirill Neklyudov View PDF HTML (experimental) Abstract:The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation

March 4, 2025

ikayaniaamirshahzad@gmail.com

How Google Research is making healthcare more accessible and personalized with AI

Last week, at the Lake Nona Impact Forum for advancing global health, I discussed the potential of AI to meaningfully improve healthcare and advance science. Our recent AI breakthroughs provide unprecedented opportunities to make healthcare more accessible, personalized and effective for everyone, and to significantly accelerate scientific discovery. Here’s an update on our progress, how we’re collaborating with partners to bring AI to global healthcare settings and our recently announced AI co-scientist. AI is making accurate health information more accessible Google is often the first place people turn to when they are looking for answers to health-related questions, so we

March 4, 2025

ikayaniaamirshahzad@gmail.com

Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem – Machine Learning Blog | ML@CMU

Figure 1: Training models to optimize test-time compute and learn “how to discover” correct responses, as opposed to the traditional learning paradigm of learning “what answer” to output. The major strategy to improve large language models (LLMs) thus far has been to use more and more high-quality data for supervised fine-tuning (SFT) or reinforcement learning (RL). Unfortunately, it seems this form of scaling will soon hit a wall, with the scaling laws for pre-training plateauing, and with reports that high-quality text data for training maybe exhausted by 2028, particularly for more difficult tasks, like solving reasoning problems which seems to

March 4, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

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March 4, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 4, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 4, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 4, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 4, 2025

ikayaniaamirshahzad@gmail.com

[D] LF Data annotators for machine learning

Hey everyone! I’m working on a computer vision project that’s giving me a bit of a headache. I’m building a custom object detection model for a pretty niche use case: identifying and classifying industrial machine parts (screws, bolts, and custom components) in low-light factory environments. It’s not something I can just pull off the shelf from a public dataset, and automated labeling tools are struggling because the parts are often overlapping, partially obscured, or look super similar to each other. After wrestling with this for a while, I’ve come to the conclusion that I need to go the manual labeling

March 4, 2025

ikayaniaamirshahzad@gmail.com

Probabilistic time series forecasting with compositional bayesian neural networks

AutoBNN is based on a line of research that over the past decade has yielded improved predictive accuracy by modeling time series using GPs with learned kernel structures. The kernel function of a GP encodes assumptions about the function being modeled, such as the presence of trends, periodicity or noise. With learned GP kernels, the kernel function is defined compositionally: it is either a base kernel (such as Linear, Quadratic, Periodic, Matérn or ExponentiatedQuadratic) or a composite that combines two or more kernel functions using operators such as Addition, Multiplication, or ChangePoint. This compositional kernel structure serves two related purposes.