Trending Research
Robust Speech Recognition via Large-Scale Weak Supervision
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet.
Ranked #1 on
Speech Recognition
on Common Voice Italian
(using extra training data)
Pre-training Small Base LMs with Fewer Tokens
Here we show that smaller LMs trained utilizing some of the layers of GPT2-medium (355M) and GPT-2-large (770M) can effectively match the val loss of their bigger counterparts when trained from scratch for the same number of training steps on OpenWebText dataset with 9B tokens.
Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues
SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types.
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization.
AutoCodeRover: Autonomous Program Improvement
Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding.
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples
We analyze how well pre-trained large language models (e. g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates.
SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System
To this end, we propose a novel filter-based VINS framework named SchurVINS, which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement.
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%.
Multivariate Time Series Forecasting
Representation Learning
+2
SafeGen: Mitigating Unsafe Content Generation in Text-to-Image Models
letterligo/text-agnostic-governance
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The key idea is to eliminate unsafe visual representations from the model regardless of the text input.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
