Recent advances in vision–language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independent agents that reason and act purely from visual input remains underexplored. We investigate this setting using Qwen2.5-VL-32B, one of the strongest open-source VLMs available, and focus on improving its reliability in web-based control. Through initial experimentation, we observe three key challenges: (i)~inaccurate localization of target elements, the cursor, and their relative positions, (ii)~sensitivity to instruction phrasing, and (iii)~an overoptimistic bias toward its own actions, often assuming they succeed rather than analyzing their actual outcomes. To address these issues, we fine-tune Qwen2.5-VL-32B for a basic web interaction task: moving the mouse and clicking on a page element described in natural language. Our training pipeline consists of two stages: (1)~teaching the model to determine whether the cursor already hovers over the target element or whether movement is required, and (2)~training it to execute a single command (a mouse move or a mouse click) at a time, verifying the resulting state of the environment before planning the next action. Evaluated on a custom benchmark of single-click web tasks, our approach increases success rates from 86% to 94% under the most challenging setting.

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 inaccuracies of the learned model. We propose to regularize trajectory optimization by means of a denoising autoencoder that is trained on the same trajectories as the model of the environment. We show that the proposed regularization leads to improved planning with both gradient-based and gradient-free optimizers. We also demonstrate that using regularized trajectory optimization leads to rapid initial learning in a set of popular motor control tasks, which suggests that the proposed approach can be a useful tool for improving sample efficiency.

We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The architecture shows close-to-optimal results on temporal modeling of video data, competitive results on music modeling, and improved perceptual grouping based on higher order abstractions, such as stochastic textures and motion cues. We present results for fully supervised, semi-supervised, and unsupervised tasks. The results suggest that the proposed architecture and principles are powerful tools for learning a hierarchy of abstractions, learning iterative inference and handling temporal information.

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.

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 segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. We enable a neural network to group the representations of different objects in an iterative manner through a differentiable mechanism. We achieve very fast convergence by allowing the system to amortize the joint iterative inference of the groupings and their representations. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. We evaluate our method on multi-digit classification of very cluttered images that require texture segmentation. Remarkably our method achieves improved classification performance over convolutional networks despite being fully connected, by making use of the grouping mechanism. Furthermore, we observe that our system greatly improves upon the semi-supervised result of a baseline Ladder network on our dataset. These results are evidence that grouping is a powerful tool that can help to improve sample efficiency.

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 cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Valpola (2015) which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels.

We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large variety of latent variable models can be constructed from these blocks, including nonlinear and variance models, which are lacking from most existing variational systems. The introduced blocks are designed to fit together and to yield efficient update rules. Practical implementation of various models is easy thanks to an associated software package which derives the learning formulas automatically once a specific model structure has been fixed. Variational Bayesian learning provides a cost function which is used both for updating the variables of the model and for optimising the model structure. All the computations can be carried out locally, resulting in linear computational complexity. We present experimental results on several structures, including a new hierarchical nonlinear model for variances and means. The test results demonstrate the good performance and usefulness of the introduced method.

In this paper, we introduce a modeling approach called independent variable group analysis (IVGA) which can be used for finding an efficient structural representation for a given data set. The basic idea is to determine such a grouping for the variables of the data set that mutually dependent variables are grouped together whereas mutually independent or weakly dependent variables end up in separate groups. Computation of an IVGA model requires a combinatorial algorithm for grouping of the variables and a modeling algorithm for the groups. In order to be able to compare different groupings, a cost function which reflects the quality of a grouping is also required. Such a cost function can be derived, for example, using the variational Bayesian approach, which is employed in our study. This approach is also shown to be approximately equivalent to minimizing the mutual information between the groups. The modeling task is computationally demanding. We describe an efficient heuristic grouping algorithm for the variables and derive a computationally light nonlinear mixture model for modeling of the dependencies within the groups. Finally, we carry out a set of experiments which indicate that IVGA may turn out to be beneficial in many different applications.

We present a method for exploratory data analysis of large spatiotemporal data sets such as global longtime climate measurements, extending our previous work on semiblind source separation of climate data. The method seeks fast changing components whose variances exhibit slow behavior with specific temporal structure. The algorithm is developed in the framework of denoising source separation. It finds sources iteratively and alternates between estimating the variance structure of extracted sources and using the structure to find new source estimates. The performance of the algorithm is first demonstrated on a simple example of a semiblind source separation problem with artificially generated signals. Then, the proposed technique is applied to the global surface temperature measurements coming from the NCEP/NCAR re-analysis project. Fast changing temperature components whose variances have prominent annual and decadal structures are extracted. The extracted annual components reflect higher temperature variability over the continents during winters. The components with slower changing variances might correspond to some interesting weather phenomena characterized by slowly changing temperature variability in specific regions.

Denoising source separation (DSS), a recently developed source separation framework, was applied to extracting components exhibiting slow, interannual temporal behavior from climate data. Three datasets with daily measurements were used: surface temperature, sea level pressure and precipitation around the globe. For all datasets, the first extracted component captured the well-known El Nino-Southern Oscillation phenomenon and the second component was close to the derivative of the first one. Several other components with slow dynamics were extracted and together the components appear to capture essential features of the slow-dynamics state of the climate system. The first two components were identified reliably but the following components may have remained mixed, nonlinear DSS could identify the physically most meaningful rotation among them but only linear DSS was within the scope of this paper. This paper offers a simple demonstration of exploratory data analysis of climate data by DSS and suggests future lines of research.

A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for the easy development of new source separation algorithms which can be optimised for specific problems. In this framework, source separation algorithms are constructed around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are considered. Some existing independent component analysis algorithms are reinterpreted within the DSS framework and new, robust blind source separation algorithms are suggested. The framework is derived as a one-unit equivalent to an EM algorithm for source separation. However, in the DSS framework it is easy to utilise various kinds of denoising procedures which need not be based on generative models. In the experimental section, various DSS schemes are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and over-complete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.

In this paper we present a framework for using multi-layer per- ceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is handled by linearizing it using a Gauss–Hermite quadrature at the hidden neu- rons. This yields an accurate approximation for cases of large pos- terior variance. The method can be used to derive nonlinear coun- terparts for linear algorithms such as factor analysis, independent component/factor analysis and state-space models. This is demon- strated with a nonlinear factor analysis experiment in which even 20 sources can be estimated from a real world speech data set.

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.

Changes in a dynamical process are often detected by monitoring selected indicators directly obtained from the process observations, such as the mean values or variances. Standard change detection algorithms such as the Shewhart control charts or the cumulative sum (CUSUM) algorithm are often based on such first- and second-order statistics. Much better results can be obtained if the dynamical process is properly modeled, for example by a nonlinear state-space model, and then the accuracy of the model is monitored over time. The success of the latter approach depends largely on the quality of the model. In practical applications like industrial processes, the state variables, dynamics, and observation mapping are rarely known accurately. Learning from data must be used; however, methods for the simultaneous estimation of the state and the unknown nonlinear mappings are very limited. We use a novel method of learning a nonlinear state-space model, the nonlinear dynamical factor analysis (NDFA) algorithm. It takes a set of multivariate observations over time and fits blindly a generative dynamical latent variable model, resembling nonlinear independent component analysis. We compare the performance of the model in process change detection to various traditional methods. It is shown that NDFA outperforms the classical methods by a wide margin in a variety of cases where the underlying process dynamics changes.

A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear mapping from unknown factors. The dynamics of the factors are modeled using a nonlinear state-space model. The nonlinear mappings in the model are represented using multilayer perceptron networks. The proposed method is computationally demanding, but it allows the use of higher-dimensional nonlinear latent variable models than other existing approaches. Experiments with chaotic data show that the new method is able to blindly estimate the factors and the dynamic process that generated the data. It clearly outperforms currently available nonlinear prediction techniques in this very difficult test problem.