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Found 9 papers, 4 papers with code
When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization
Contemporary machine learning models, such as language models, are powerful, but come with immense resource requirements both at training and inference time.
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks
We further investigate the robustness of 1. 58-bit quantization-aware training to changes in the learning rate and regularization through weight decay, finding different patterns for small language and vision models than previously reported for large language models.
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks
This paper explores the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, with a broad focus on Germanic languages.
SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
With the growing demand for synthetic data to address contemporary issues in machine learning, such as data scarcity, data fairness, and data privacy, having robust tools for assessing the utility and potential privacy risks of such data becomes crucial.
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
We study the applicability and usefulness of the CAIR principles and rubric by assessing a selection of metrics popular in other studies.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging
We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Multi-Sense Language Modelling
The effectiveness of a language model is influenced by its token representations, which must encode contextual information and handle the same word form having a plurality of meanings (polysemy).
