2 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Australian National UniversityAU1 paper
- Canada–France–Hawaii TelescopeUS1 paper
- Carnegie ObservatoriesUS1 paper
- Centre BorelliFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- École Normale Supérieure - PSLFR1 paper
- Herzberg Institute of AstrophysicsCA1 paper
- Institute for Advanced StudyUS1 paper
- International Centre for Radio Astronomy ResearchAU1 paper
- National Academies of Sciences, Engineering, and MedicineUS1 paper
- Netherlands Institute for Radio AstronomyNL1 paper
- Observatoire de la Côte d’AzurFR1 paper
7 papers
A Deterministic Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving
Ian D'Ambrosio
Dynamic Mixture-of-Experts Serving allocates k replica GPUs among m experts as workloads change. At each round, the online algorithm sees the current workload, chooses integral rep…
A Tight Linear Deterministic Competitive Ratio for Fully Online KV-Cache Scheduling
Ian D'Ambrosio
Jaillet et al. introduced a fully online model for batching nonpreemptive LLM requests under a growing KV-cache memory constraint. For total end-to-end latency they proved that eve…
Sample-Size Scaling of the African Languages NLI Evaluation
Anuj Tiwari, Oluwapelumi Ogunremu, Terry Oko-odion +2
African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performance. The study is a systemati…
Extraction of Secrets from 40nm CMOS Gate Dielectric Breakdown Antifuses by FIB Passive Voltage Contrast
Andrew D. Zonenberg, Antony Moor, Daniel Slone +2
CMOS one-time-programmable (OTP) memories based on antifuses are widely used for storing small amounts of data (such as serial numbers, keys, and factory trimming) in integrated ci…
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
Sankalp Gilda
Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advan…
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt +1
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This…
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
Sankalp Gilda, Stark C. Draper, Sebastien Fabbro +6
We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and ob…