Vasileios Lioutas — accepted AI-conference papers

2024

Nearest Neighbour Score Estimators for Diffusion Generative Models

Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang +6

ICML 2024poster

Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a nov…

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2023

A Diffusion-Model of Joint Interactive Navigation

Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios Lioutas, Justice Sefas, Xiaoxuan Liang +5

NeurIPS 2023poster

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios…

Cited by 15 Source ↗PDF ↗Scholar ↗

2023

Critic Sequential Monte Carlo

Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg +3

ICLR 2023poster

We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards t…

Cited by 9 Source ↗PDF ↗Scholar ↗

2021

MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation

Ahmad Rashid, Vasileios Lioutas, Mehdi Rezagholizadeh

ACL 2021long

The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them pr…

Cited by 36 Source ↗PDF ↗Scholar ↗

2021

Towards Zero-Shot Knowledge Distillation for Natural Language Processing

Ahmad Rashid, Vasileios Lioutas, Abbas Ghaddar, Mehdi Rezagholizadeh

EMNLP 2021main

Knowledge distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions. In its regular manifestations, KD requires access to the teacher’s training data for knowledge transfer to the student…

Cited by 28 Source ↗PDF ↗Scholar ↗

2020

Time-aware Large Kernel Convolutions

Vasileios Lioutas, Yuhong Guo

ICML 2020poster

To date, most state-of-the-art sequence modeling architectures use attention to build generative models for language based tasks. Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$. Alternatively, they utilize…

Cited by 34 Source ↗PDF ↗code ↗Scholar ↗