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Found 9 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)
PEEKABOO: Interactive Video Generation via Masked-Diffusion
In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control.
Unlocking Spatial Comprehension in Text-to-Image Diffusion Models
We propose CompFuser, an image generation pipeline that enhances spatial comprehension and attribute assignment in text-to-image generative models.
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Construction of a universal detector poses a crucial question: How can we most effectively train a model on a large mixture of datasets?
Efficiently Robustify Pre-trained Models
A recent trend in deep learning algorithms has been towards training large scale models, having high parameter count and trained on big dataset.
Generalized Decoding for Pixel, Image, and Language
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly.
Ranked #4 on
Instance Segmentation
on ADE20K val
(using extra training data)
Neural-Sim: Learning to Generate Training Data with NeRF
However, existing approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts of random data variations, which is slow and is often suboptimal for the target domain.
STEER : Simple Temporal Regularization For Neural ODE
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
