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pro vyhledávání: '"Schifferer, Benedikt"'
Autor:
Moreira, Gabriel de Souza P., Ak, Ronay, Schifferer, Benedikt, Xu, Mengyao, Osmulski, Radek, Oldridge, Even
Ranking models play a crucial role in enhancing overall accuracy of text retrieval systems. These multi-stage systems typically utilize either dense embedding models or sparse lexical indices to retrieve relevant passages based on a given query, foll
Externí odkaz:
http://arxiv.org/abs/2409.07691
Autor:
Deotte, Chris, Sorokin, Ivan, Erdem, Ahmet, Schifferer, Benedikt, Titericz Jr, Gilberto, Jegou, Simon
This paper describes the winning solution of all 5 tasks for the Amazon KDD Cup 2024 Multi Task Online Shopping Challenge for LLMs. The challenge was to build a useful assistant, answering questions in the domain of online shopping. The competition c
Externí odkaz:
http://arxiv.org/abs/2408.04658
Autor:
Moreira, Gabriel de Souza P., Osmulski, Radek, Xu, Mengyao, Ak, Ronay, Schifferer, Benedikt, Oldridge, Even
Text embedding models have been popular for information retrieval applications such as semantic search and Question-Answering systems based on Retrieval-Augmented Generation (RAG). Those models are typically Transformer models that are fine-tuned wit
Externí odkaz:
http://arxiv.org/abs/2407.15831
Successful training of deep neural networks with noisy labels is an essential capability as most real-world datasets contain some amount of mislabeled data. Left unmitigated, label noise can sharply degrade typical supervised learning approaches. In
Externí odkaz:
http://arxiv.org/abs/2109.14563
Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple chal
Externí odkaz:
http://arxiv.org/abs/2004.00685
Autor:
Diodato, Michael, Li, Yu, Lovjer, Antonia, Yeom, Minsu, Song, Albert, Zeng, Yiyang, Khosla, Abhay, Schifferer, Benedikt, Goyal, Manik, Drori, Iddo
Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving. We demonstrate the best predicted angle, speed, and best performance overall winning the top three places of the ICCV 2019 Learning to Drive challenge. Ou
Externí odkaz:
http://arxiv.org/abs/1911.08568
Publikováno v:
ICCV Autonomous Driving Workshop, 2019
In this work we predict vehicle speed and steering angle given camera image frames. Our key contribution is using an external pre-trained neural network for segmentation. We augment the raw images with their segmentation masks and mirror images. We e
Externí odkaz:
http://arxiv.org/abs/1910.10317