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Found 12 papers, 8 papers with code
Layer by Layer: Uncovering Hidden Representations in Language Models
From extracting features to generating text, the outputs of large language models (LLMs) typically rely on their final layers, following the conventional wisdom that earlier layers capture only low-level cues.
Does Representation Matter? Exploring Intermediate Layers in Large Language Models
Understanding what defines a good representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications.
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
To address this, we propose Sequential Variance-Covariance Regularization (Seq-VCR), which enhances the entropy of intermediate representations and prevents collapse.
VFA: Vision Frequency Analysis of Foundation Models and Human
Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation.
Unsupervised Concept Discovery Mitigates Spurious Correlations
Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases.
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labelled data was available.
Challenging Common Assumptions about Catastrophic Forgetting
In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence.
Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios.
A Statistical Real-Time Prediction Model for Recommender System
Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites.
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
