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Found 11 papers, 5 papers with code
TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers.
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
Confirming this, under a gaussian mixture setting, we show that the optimal SVM classifier for balanced accuracy needs to be adaptive to the class attributes.
FedYolo: Augmenting Federated Learning with Pretrained Transformers
These insights on scale and modularity motivate a new federated learning approach we call "You Only Load Once" (FedYolo): The clients load a full PTF model once and all future updates are accomplished through communication-efficient modules with limited catastrophic-forgetting, where each task is assigned to its own module.
Max-Margin Token Selection in Attention Mechanism
Interestingly, the SVM formulation of $\boldsymbol{p}$ is influenced by the support vector geometry of $\boldsymbol{v}$.
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
To address this shortcoming, in this paper we study a class of neural ordinary differential equations that, by design, leave a given manifold invariant, and characterize their properties by leveraging the controllability properties of control affine systems.
AutoBalance: Optimized Loss Functions for Imbalanced Data
Our experimental findings are complemented with theoretical insights on loss function design and the benefits of train-validation split.
Post-hoc Models for Performance Estimation of Machine Learning Inference
Estimating how well a machine learning model performs during inference is critical in a variety of scenarios (for example, to quantify uncertainty, or to choose from a library of available models).
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer
Recent end-to-end deep neural networks for disparity regression have achieved the state-of-the-art performance.
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution.
Ranked #14 on
Image Super-Resolution
on Set14 - 3x upscaling
Lightweight Feature Fusion Network for Single Image Super-Resolution
Specifically, a spindle block is composed of a dimension extension unit, a feature exploration unit and a feature refinement unit.
Ranked #11 on
Image Super-Resolution
on Manga109 - 3x upscaling
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.
