23 citations · 58 across the 11 of their papers we have counts for
4 papers · 1 filter
Federated Lightweight Fine-Tuning
Radhakrishna Achanta, Will Reed
Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by onl…
TallyTrain: Communication-Efficient Federated Distillation
Radhakrishna Achanta, Will Reed
Federated learning is bandwidth-bound on two orthogonal axes: model size, which limits how often parameter-averaging methods can afford to merge, and class count, which makes per-p…
Uncertainty Surrogates for Deep Learning
Radhakrishna Achanta, Natasa Tagasovska
In this paper we introduce a novel way of estimating prediction uncertainty in deep networks through the use of uncertainty surrogates. These surrogates are features of the penulti…
Deep Feature Factorization For Concept Discovery
Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep con…