582 citations · 739 across the 8 of their papers we have counts for
8 papers
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Guoli Yin, Haoping Bai, Shuang Ma +21
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, whi…
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund +4
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting ina…
Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff, Antti Rasmus, Mathias Berglund +3
We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segm…
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff +1
Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for loc…
Semi-Supervised Learning with Ladder Networks
Antti Rasmus, Harri Valpola, Mikko Honkala +2
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised c…
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Mathias Berglund, Tapani Raiko, Mikko Honkala +3
Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously. They have not been used commonly in unsupervi…
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Tapani Raiko, Mathias Berglund, Guillaume Alain +1
Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn…
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence
Mathias Berglund, Tapani Raiko
Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are popular methods for training the weights of Restricted Boltzmann Machines. However, both methods use an…