paper_id uint32 0 3.7k | title stringlengths 14 154 | paper_url stringlengths 42 42 | authors listlengths 1 21 | type stringclasses 3
values | abstract stringlengths 413 2.52k | keywords stringlengths 4 397 | TL;DR stringlengths 5 250 ⌀ | submission_number int64 2 14.3k | arxiv_id stringlengths 10 10 ⌀ | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|
3,700 | Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation | https://openreview.net/forum?id=qPx3i9sMxv | [
"Peiwen Sun",
"Sitong Cheng",
"Xiangtai Li",
"Zhen Ye",
"Huadai Liu",
"Honggang Zhang",
"Wei Xue",
"Yike Guo"
] | Spotlight | Recently, diffusion models have achieved great success in mono-channel audio generation.
However, when it comes to stereo audio generation, the soundscapes often have a complex scene of multiple objects and directions.
Controlling stereo audio with spatial contexts remains challenging due to high data costs and unstabl... | audio generation, multimodal learning, stereo audio | The multi-modal guided spatial audio generation dataset and method for immersive soundscapes | 102 | 2410.10676 | [
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3,701 | Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation | https://openreview.net/forum?id=OdnqG1fYpo | [
"Qing Wu",
"Chenhe Du",
"Xuanyu Tian",
"Jingyi Yu",
"Yuyao Zhang",
"Hongjiang Wei"
] | Spotlight | Motion correction (MoCo) in radial MRI is a particularly challenging problem due to the unpredictability of subject movement. Current state-of-the-art (SOTA) MoCo algorithms often rely on extensive high-quality MR images to pre-train neural networks, which constrains the solution space and leads to outstanding image re... | MRI Reconstruction, Motion Correction, Neural Representation, NeRF, Unsupervised Learning | We propose Moner, an unsupervised method that can jointly recover high-quality MR images and estimates accurate motion from undersampled and rigid motion-corrupted radial MRI measurement data without the need for any extra data. | 101 | 2409.16921 | [
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3,702 | UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery | https://openreview.net/forum?id=v9EjwMM55Y | [
"Ruifeng Li",
"Mingqian Li",
"Wei Liu",
"Yuhua Zhou",
"Xiangxin Zhou",
"Yuan Yao",
"Qiang Zhang",
"Hongyang Chen"
] | Spotlight | Drug discovery is crucial for identifying candidate drugs for various diseases. However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine differ... | Few-shot molecular representation learning, maching learning | We introduce HierMatch, which performs matching across multiple levels, from atoms to tasks, to enhance molecular property predic- tions in few-shot learning scenarios. | 45 | 2502.12453 | [
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3,703 | OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes | https://openreview.net/forum?id=L6IgkJvcgV | [
"Sepehr Dehdashtian",
"Gautam Sreekumar",
"Vishnu Boddeti"
] | Spotlight | Images generated by text-to-image (T2I) models often exhibit visual biases and stereotypes of concepts such as culture and profession. Existing quantitative measures of stereotypes are based on statistical parity that does not align with the sociological definition of stereotypes and, therefore, incorrectly categorizes... | Stereotype Measurement, Responsible AI, Trustworthy AI, Interpretability, Generative AI, Text-to-Image Models, Multimodal Models | We propose a toolbox to quantify stereotypes in Text-to-Image models | 17 | 2501.00962 | [
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3,704 | Instance-dependent Early Stopping | https://openreview.net/forum?id=P42DbV2nuV | [
"Suqin Yuan",
"Runqi Lin",
"Lei Feng",
"Bo Han",
"Tongliang Liu"
] | Spotlight | In machine learning practice, early stopping has been widely used to regularize models and can save computational costs by halting the training process when the model's performance on a validation set stops improving. However, conventional early stopping applies the same stopping criterion to all instances without cons... | Early Stopping, Supervised Learning, Deep Learning, Efficiency, Sample Selection, Data Pruning | We propose an instance-dependent early stopping method that stops training at the instance level by determining whether the model has fully learned an instance. It reduces computational costs while maintaining or even improving model performance. | 2 | 2502.07547 | [
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