no code implementations • 27 Oct 2024 • Ivan Kukanov, Janne Laakkonen, Tomi Kinnunen, Ville Hautamäki
Current speech deepfake detection approaches perform satisfactorily against known adversaries; however, generalization to unseen attacks remains an open challenge.
no code implementations • 25 Sep 2024 • Marko Tuononen, Dani Korpi, Ville Hautamäki
We propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model.
no code implementations • 20 Mar 2024 • Yusuke Mikami, Andrew Melnik, Jun Miura, Ville Hautamäki
We demonstrate experimental results with LLMs that address robotics task planning problems.
no code implementations • 23 Mar 2023 • Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Sharada Mohanty, Byron Galbraith, Ke Chen, Yan Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv, Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik, Shu Ishida, João F. Henriques, Robert Klassert, Walter Laurito, Ellen Novoseller, Vinicius G. Goecks, Nicholas Waytowich, David Watkins, Josh Miller, Rohin Shah
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
no code implementations • 27 Dec 2022 • Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik
Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
no code implementations • 27 Oct 2022 • Ruijie Tao, Kong Aik Lee, Rohan Kumar Das, Ville Hautamäki, Haizhou Li
We study a novel neural architecture and its training strategies of speaker encoder for speaker recognition without using any identity labels.
1 code implementation • 14 May 2022 • Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating.
no code implementations • 9 Mar 2022 • Trung Ngo, Najwa Laabid, Ville Hautamäki, Merja Heinäniemi
Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regularization coefficient for disentangling representation but ultimately generating blurry examples.
no code implementations • 24 Jan 2022 • Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen, Junichi Yamagishi
As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve security.
no code implementations • 19 Jan 2022 • Federico Malato, Joona Jehkonen, Ville Hautamäki
Behavioural cloning has been extensively used to train agents and is recognized as a fast and solid approach to teach general behaviours based on expert trajectories.
1 code implementation • 8 Oct 2021 • Ruijie Tao, Kong Aik Lee, Rohan Kumar Das, Ville Hautamäki, Haizhou Li
In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals.
1 code implementation • 1 Jul 2021 • Anssi Kanervisto, Christian Scheller, Yanick Schraner, Ville Hautamäki
Reinforcement learning (RL) research focuses on general solutions that can be applied across different domains.
no code implementations • 21 Apr 2021 • Keishi Ishihara, Anssi Kanervisto, Jun Miura, Ville Hautamäki
This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.
no code implementations • 8 Dec 2020 • Ivan Kukanov, Janne Karttunen, Hannu Sillanpää, Ville Hautamäki
Since the invention of cinema, the manipulated videos have existed.
1 code implementation • 2 Dec 2020 • Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Behavioural characterizations (BCs) of decision-making agents, or their policies, are used to study outcomes of training algorithms and as part of the algorithms themselves to encourage unique policies, match expert policy or restrict changes to policy per update.
1 code implementation • 7 May 2020 • Anssi Kanervisto, Janne Karttunen, Ville Hautamäki
MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment.
1 code implementation • 2 Apr 2020 • Anssi Kanervisto, Christian Scheller, Ville Hautamäki
In this work, we aim to gain insight on these action space modifications by conducting extensive experiments in video-game environments.
1 code implementation • 2 Apr 2020 • Anssi Kanervisto, Joonas Pussinen, Ville Hautamäki
We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data.
1 code implementation • 6 Feb 2020 • Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen, Junichi Yamagishi
The spoofing countermeasure (CM) systems in automatic speaker verification (ASV) are not typically used in isolation of each other.
1 code implementation • 6 Jul 2019 • Bilal Soomro, Anssi Kanervisto, Trung Ngo Trong, Ville Hautamäki
One such debugging method used with image classification DNNs is activation maximization, which generates example-images that are classified as one of the classes.
1 code implementation • ICML Workshop on Computational Biology 2019 2019 • Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo González, Ville Hautamäki, Merja Heinäniemi
In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model.
1 code implementation • 2 May 2019 • Janne Karttunen, Anssi Kanervisto, Ville Kyrki, Ville Hautamäki
Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training.
1 code implementation • 8 Nov 2018 • Ville Vestman, Bilal Soomro, Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen
The popularization of science can often be disregarded by scientists as it may be challenging to put highly sophisticated research into words that general public can understand.
Audio and Speech Processing Sound
1 code implementation • 26 Jul 2018 • Anssi Kanervisto, Ville Hautamäki
We present Toribash Learning Environment (ToriLLE), a learning environment for machine learning agents based on the video game Toribash.
no code implementations • 30 Apr 2018 • Trung Ngo Trong, Ville Hautamäki, Kristiina Jokinen
Language recognition system is typically trained directly to optimize classification error on the target language labels, without using the external, or meta-information in the estimation of the model parameters.
no code implementations • 5 Feb 2016 • Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).