Customer-obsessed science
Research areas
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July 9, 202610 min readA new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts. -
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The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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IEEE ICIP 20232023Semi-supervised learning (SSL) has become a crucial approach in deep learning as a way to address the challenge of limited labeled data. The success of deep neural networks heavily relies on the availability of large-scale high-quality labeled data. However, the process of data labeling is time-consuming and unscalable, leading to shortages in labeled data. SSL aims to tackle this problem by leveraging
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ECML PKDD 20232023Timestamped graphs find applications in critical business problems like user classification, fraud detection, etc. This is due to the inherent nature of the data generation process, in which relationships between nodes are observed at defined timestamps. Deployment-focused GNN models should be trained on point-in-time information about node features and neighborhood, similar to the data ingestion process
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ECML PKDD 20232023Causal Impact (CI) measurement is broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate average and conditional average treatment effects across 100s of customer action types for e-commerce and digital businesses and 100s of millions of customers that can be used in decisions
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ACL Findings 20232023There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022). However, existing methods typically encode task information with a simple dataset name as a prefix to the encoder. This not only limits the effectiveness of multitask learning, but also hinders the model’s ability to generalize to new domains or tasks
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SIGIR 2023 Workshop on eCommerce2023Learning-to-rank models are mostly evaluated based on how good it is able to estimate the user behaviour. Output metrics like NDCG become the obvious choice for the purpose. A model is considered to have a good performance if it is able to predict the correct ranked ordering, else it is considered to be of poor quality. However the performance of a model is not only dependent on the prediction power of
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Gretchen Ertl
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A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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