Lotus
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LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
Announce Type: replace Abstract: We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from scratch. LoTUS smooths the prediction probabilities of the model up to an information-theoretic bound, mitigating its over-confidence stemming from data memorization. We evaluate LoTUS on Transformer and ResNet18 models against eight baselines across five public datasets.
Bridge the Last-Mile Gap to Semantic Analytics: Compiling Natural-Language Queries into Semantic Operator Pipelines
arXiv:2606.04641v1 Announce Type: new Abstract: Automated AI workflows increasingly rely on natural-language reasoning over heterogeneous data, but lack a practical way to execute it through optimized semantic data systems. Recent semantic operator systems, such as Palimpzest and LOTUS, expose declarative operators for filtering, joining, mapping, and aggregating over tables, text, and images using natural-language predicates. However, these systems require users to manually choose...
From Schitt's Creek to Desi Bling: Is cringe-binge actually about self-care?
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Ask HN: What are tools you have made for yourself since the advent of AI?
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From Chiang Mai’s black house to the Bangkok skyline: Inside 137 Pillars
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Milestone makers reflect on McLaren's decades of success
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Amazing interior, controversial exterior: Ferrari's first electric car
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