March 19, 2025
I was learning Deep Learning. To clear the mathematical foundations, I learnt about gradient, the basis for gradient descent algorithm. Gradient comes under vector calculus. Along the way, I realised that I need a good reference book for vector calculus. Please suggest some good reference books for vector calculus. submitted by /u/Master_Jacket_4893 [comments] Source link
March 19, 2025
As a part of Face Recognition Project, we are implementing this using CCTV, currently using FAISS as vector Database, but it continoulsy giving wrong recongition, is there any better db or method to improove face recognition submitted by /u/TalkLate529 [comments] Source link
March 19, 2025
submitted by /u/Efistoffeles [comments] Source link
March 19, 2025
Would anyone be interested in being featured on a blog article? I'm looking to have an interview with someone versed in A.I. & machine learning to have a conversation with. I'm working on a blog/research article titled: When Machines Become Gods: How Al ls Reshaping Faith and Forging a New Era of Technocratic Religion. submitted by /u/Cicada01123 [comments] Source link
March 19, 2025
Hello everyone im currently doing an internship as an ML intern and I'm working on fraud detection with 100ms inference time. The issue I'm facing is that the class imbalance in the data is causing issues with precision and recall. My class imbalance is as follows: Is Fraudulent 0 1119291 1 59070 I have done feature engineering on my dataset and i have a total of 51 features. There are no null values and i have removed the outliers. To handle class imbalance I have tried versions of SMOTE , mixed architecture of various under samplers and over samplers. I
March 19, 2025
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This is a summary of: Sheinin, R. et al. scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions. Nat. Methods. https://doi.org/10.1038/s41592-025-02627-0 (2025). Source link
March 19, 2025
We tuned GPT-Neo on eval harness tasks to see how it would change its performance. Source link
March 19, 2025
Responsible by design Gemma is designed with our AI Principles at the forefront. As part of making Gemma pre-trained models safe and reliable, we used automated techniques to filter out certain personal information and other sensitive data from training sets. Additionally, we used extensive fine-tuning and reinforcement learning from human feedback (RLHF) to align our instruction-tuned models with responsible behaviors. To understand and reduce the risk profile for Gemma models, we conducted robust evaluations including manual red-teaming, automated adversarial testing, and assessments of model capabilities for dangerous activities. These evaluations are outlined in our Model Card. We’re also releasing a
March 19, 2025
[Submitted on 24 May 2023 (v1), last revised 17 Mar 2025 (this version, v4)] View a PDF of the paper titled ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models, by Suzanna Parkinson and 2 other authors View PDF HTML (experimental) Abstract:Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is, training the network effectively learns an interpolating function, and properties of the interpolant affect predictions the network will make on new samples. This manuscript explores how properties
March 19, 2025
[Submitted on 18 Feb 2025 (v1), last revised 18 Mar 2025 (this version, v2)] View a PDF of the paper titled Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation, by Bencheng Liao and Hongyuan Tao and Qian Zhang and Tianheng Cheng and Yingyue Li and Haoran Yin and Wenyu Liu and Xinggang Wang View PDF HTML (experimental) Abstract:Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native