Customer-obsessed science


Research areas
-
June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
Featured news
-
2024By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities. However, the generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as hallucination of nonexistent scene elements, missing
-
2024Recent video masked autoencoder (MAE) works have de-signed improved masking algorithms focused on saliency. These works leverage visual cues such as motion to mask the most salient regions. However, the robustness of such visual cues depends on how often input videos match underlying assumptions. On the other hand, natural language description is an information dense representation of video that implicitly
-
IEEE Sensors2024Continuous back posture monitoring and correction can help to prevent back pains associated with improper back postures. However, existing solutions are expensive, use wearable sensors which usually require regular maintenance, or use cameras which have privacy issues. We introduce Di-Angle, a low-cost, battery-free, and reusable sensor capable of monitoring harmful back angles with high accuracy. Our novel
-
2024A complementary item is an item that pairs well with another item when consumed together. In the context of e-commerce, providing recommendations for complementary items is essential for both customers and stores. Current models for suggesting complementary items often rely heavily on user behavior data, such as co-purchase relationships. However, just because two items are frequently bought together does
-
2024Handling drafty partial code remains a notable challenge in real-time code suggestion applications. Previous work has demonstrated shortcomings of large language models of code (CodeLLMs) in completing partial code with potential bugs. In this study, we view partial code as implementation hints and fine-tune CodeLLMs to jointly rewrite and complete partial code into functional full programs. We explore
Academia
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all