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Found 10 papers, 4 papers with code
Phi-4 Technical Report
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality.
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Textbooks Are All You Need
Despite this small scale, phi-1 attains pass@1 accuracy 50. 6% on HumanEval and 55. 5% on MBPP.
Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks.
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.
Ranked #1 on
Object Detection
on ELEVATER
A Contrastive Learning Approach for Training Variational Autoencoder Priors
To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior.
Ranked #6 on
Image Generation
on CelebA 256x256
(FID metric)
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior.
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning
We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future.
Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech
We achieve the trifecta: (1) High accuracy for the diverse captions as evaluated by standard captioning metrics and user studies; (2) Faster computation of diverse captions compared to beam search and diverse beam search; and (3) High diversity as evaluated by counting novel sentences, distinct n-grams and mutual overlap (i. e., mBleu-4) scores.
Convolutional Image Captioning
In recent years significant progress has been made in image captioning, using Recurrent Neural Networks powered by long-short-term-memory (LSTM) units.
