Search Results for author:
Found 17 papers, 7 papers with code
MMTEB: Massive Multilingual Text Embedding Benchmark
MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date.
Bridging the Data Provenance Gap Across Text, Speech and Video
In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sourcing trends and use restrictions to their geographical and linguistic representation.
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions.
Consent in Crisis: The Rapid Decline of the AI Data Commons
To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora.
Slight Corruption in Pre-training Data Makes Better Diffusion Models
They benefit significantly from extensive pre-training on large-scale datasets, including web-crawled data with paired data and conditions, such as image-text and image-class pairs.
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Despite these efforts, such models encounter challenges such as limited multilingual capabilities, risks of catastrophic forgetting during continual pretraining, and the high costs of training models from scratch, alongside the need to align with AI safety standards and regulatory frameworks.
Knowing Your Nonlinearities: Shapley Interactions Reveal the Underlying Structure of Data
Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models.
Just Say the Name: Online Continual Learning with Category Names Only via Data Generation
Requiring extensive human supervision is often impractical for continual learning due to its cost, leading to the emergence of 'name-only continual learning' that only provides the name of new concepts (e. g., classes) without providing supervised samples.
On the low-shot transferability of [V]-Mamba
The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples.
Uncovering the Hidden Cost of Model Compression
This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.
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.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
APP: Anytime Progressive Pruning
With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings.
Rotate to Attend: Convolutional Triplet Attention Module
In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing cross-dimension interaction using a three-branch structure.
Mish: A Self Regularized Non-Monotonic Activation Function
We propose $\textit{Mish}$, a novel self-regularized non-monotonic activation function which can be mathematically defined as: $f(x)=x\tanh(softplus(x))$.
Ranked #153 on
Image Classification
on CIFAR-100
(using extra training data)
Advanced Image Processing for Astronomical Images
Image Processing in Astronomy is a major field of research and involves a lot of techniques pertaining to improve analyzing the properties of the celestial objects or obtaining preliminary inference from the image data.
Image Processing on IOPA Radiographs: A comprehensive case study on Apical Periodontitis
With the recent advancements in Image Processing Techniques and development of new robust computer vision algorithms, new areas of research within Medical Diagnosis and Biomedical Engineering are picking up pace.
