activity
20152026
most citedLearning What and Where to Draw

210 citations · 212 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Intent-aligned Formal Specification Synthesis via Traceable Refinement

Zhe Ye, Aidan Z. H. Yang, Huangyuan Su +6

Large language models are increasingly used to generate code from natural language, but ensuring correctness remains challenging. Formal verification offers a principled way to obt…

cs.LG2026

Learning Adaptive LLM Decoding

Chloe H. Su, Zhe Ye, Samuel Tenka +3

Decoding from large language models (LLMs) typically relies on fixed sampling hyperparameters (e.g., temperature, top-p), despite substantial variation in task difficulty and uncer…

cs.AI2024★ 2 cited

Active Inference as a Model of Agency

Lancelot Da Costa, Samuel Tenka, Dominic Zhao +1

Is there a canonical way to think of agency beyond reward maximisation? In this paper, we show that any type of behaviour complying with physically sound assumptions about how macr…

math.CT2023

Additive Invariants of Open Petri Nets

Benjamin Merlin Bumpus, Sophie Libkind, Jordy Lopez Garcia +2

We classify all additive invariants of open Petri nets: these are -valued invariants which are additive with respect to sequential and parallel composition of open Petr…

cs.CV2016★ 210 cited

Learning What and Where to Draw

Scott Reed, Zeynep Akata, Santosh Mohan +3

Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, bir…

math.RT2015

A Finiteness Property of Torus Invariants

Stella Gastineau, Samuel Tenka

In this paper the invariant subring of an algebraic torus acting on the multi-homogenous polynomial ring $$S^{\boxtimes n}=\bigoplus_{d=0}^\infty (S…