Latest Research
A text-based, generative deep learning model for soil reflectance spectrum simulation in the VIS-NIR (400-2499 nm) bands
gemini-breeding/sogm_soil_spectra_simulation
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To address this, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra based on soil property inputs was developed.
Distillation for Multilingual Information Retrieval
hltcoe/colbert-x
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Recent work in cross-language information retrieval (CLIR), where queries and documents are in different languages, has shown the benefit of the Translate-Distill framework that trains a cross-language neural dual-encoder model using translation and distillation.
Faster Learned Sparse Retrieval with Block-Max Pruning
Learned sparse retrieval systems aim to combine the effectiveness of contextualized language models with the scalability of conventional data structures such as inverted indexes.
ATOM: Attention Mixer for Efficient Dataset Distillation
To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process.
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation
liamheng/non-iid_medical_image_segmentation
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To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain.
PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval
hltcoe/colbert-x
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PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, cross-language, and multilingual retrieval.
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space
We demonstrate the adaptability of our agents to novel scenarios and assembly sequences while emphasizing the potential of leveraging advanced simulation techniques for robot learning in space.
LocInv: Localization-aware Inversion for Text-Guided Image Editing
Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts.
Lying Graph Convolution: Learning to Lie for Node Classification Tasks
In this paper, we introduce Lying-GCN, a new DGN inspired by opinion dynamics that can adaptively work in both the heterophilic and the homophilic setting.
Modeling Activity-Driven Music Listening with PACE
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted.
