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Learn to enhance your LLMs with my 3 step process, inspecting, improving and iterating on your LLMs The post How to Analyze and Optimize Your LLMs in 3 Steps appeared first on Towards Data Science.

A two-year investigation by the International Confederation of Music Publishers (ICMP) and a separate analysis by The Atlantic point to a systematic double standard: Tech giants train AI on copyrighted works scraped at scale, while their own terms of service…

We’ve found the best travel cameras—from point-and-shoot to full-frame—to help you bring home the perfect vacation photos.

Alongside new iPhones, Apple released a new security architecture on Tuesday: Memory Integrity Enforcement aims to eliminate the most frequently exploited class of iOS bugs.

YouTube is rolling out multilingual audio tracks to millions of creators. The article YouTube rolls out multilingual audio tracks to millions of creators appeared first on THE DECODER.

The new pizza oven from All-Clad offers a rotating pizza stone for easy, even bakes. Turns out, I wanted this.

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

arXiv:2402.04355v3 Announce Type: replace Abstract: We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQMass, provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models. PQMass divides the sample space into non-overlapping regions and applies chi-squared tests to the number of data samples that fall within each region, giving a p-value that measures the probability that the bin counts derived from two sets of samples are drawn from the same multinomial distribution. PQMass does not depend on assumptions regarding the density of the true distribution, nor does it rely on training or fitting any auxiliary models. We evaluate PQMass on data of various modalities and dimensions, demonstrating its effectiveness in assessing the quality, novelty, and diversity of generated samples. We further show that PQMass scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.

arXiv:2503.00300v2 Announce Type: replace-cross Abstract: Operator learning is the approximation of operators between infinite dimensional Banach spaces using machine learning approaches. While most progress in this area has been driven by variants of deep neural networks such as the Deep Operator Network and Fourier Neural Operator, the theoretical guarantees are often in the form of a universal approximation property. However, the existence theorems do not guarantee that an accurate operator network is obtainable in practice. Motivated by the recent kernel-based operator learning framework, we propose a random feature operator learning method with theoretical guarantees and error bounds. The random feature method can be viewed as a randomized approximation of a kernel method, which significantly reduces the computation requirements for training. We provide a generalization error analysis for our proposed random feature operator learning method along with comprehensive numerical results. Compared to kernel-based method and neural network methods, the proposed method can obtain similar or better test errors across benchmarks examples with significantly reduced training times. An additional advantages it that our implementation is simple and does require costly computational resources, such as GPU.