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RAIVN Lab's Projects

adanns icon adanns

Code repository for the paper - "AdANNS: A Framework for Adaptive Semantic Search"

bi-att-flow icon bi-att-flow

Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.

crepe icon crepe

[CVPR23 Highlight] CREPE: Can Vision-Language Foundation Models Reason Compositionally?

deep3d icon deep3d

Automatic 2D-to-3D Video Conversion with CNNs

denspi icon denspi

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (DenSPI)

dnw icon dnw

Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)

dogtorch icon dogtorch

Who Let The Dogs Out? Modeling Dog Behavior From Visual Data https://arxiv.org/pdf/1803.10827.pdf

dqa-net icon dqa-net

Diagram question answering system described in "A Diagram is Worth a Dozen Images"

grover icon grover

Code for Defending Against Neural Fake News, https://rowanzellers.com/grover/

hellaswag icon hellaswag

HellaSwag: Can a Machine _Really_ Finish Your Sentence?

label-refinery icon label-refinery

Label Refinery: Improving ImageNet Classification through Label Progression

lcnn icon lcnn

LCNN: Lookup-based Convolutional Neural Network

matformer-olmo icon matformer-olmo

Code repository for the public reproduction of the language modelling experiments on "MatFormer: Nested Transformer for Elastic Inference"

merlot icon merlot

MERLOT: Multimodal Neural Script Knowledge Models

merlot_reserve icon merlot_reserve

Code release for "MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound"

mimic icon mimic

MIMIC: Masked Image Modeling with Image Correspondences

mnms icon mnms

m&ms: A Benchmark to Evaluate Tool-Use for multi-step multi-modal tasks

mrl icon mrl

Code repository for the paper - "Matryoshka Representation Learning"

neural-priming icon neural-priming

Code repository for the paper - "Neural Priming for Sample-Efficient Adaptation"

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