Search Results for author:
Found 8 papers, 3 papers with code
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning
In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency.
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
We investigate the combination of encoder-decoder LLMs with both encoder-decoder and decoder-only SLMs from different model families and only require fine-tuning of the SLM.
MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content.
Revisiting Single-gated Mixtures of Experts
Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time.
A Flexible Selection Scheme for Minimum-Effort Transfer Learning
Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task.
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories.
Probabilistic Image Colorization
We develop a probabilistic technique for colorizing grayscale natural images.
Classifier Adaptation at Prediction Time
Experiments on the ILSVRC2010 and ILSVRC2012 datasets show that adapting object classification systems at prediction time can significantly reduce their error rate, even with no additional human feedback.
