Algorithm transfers visual concepts to answer out-of-vocabulary questions.
problem Leveraging off-the-shelf visual and linguistic data for out-of-vocabulary answers in visual question answering.
method Unsupervised task discovery for learning task conditional visual classifier, then transferring to visual question answering models.
result Algorithm generalizes to out-of-vocabulary answers successfully.
LORL learns object-centric representations from vision and language.
problem Learning disentangled, object-centric scene representations from vision and language.
method LORL integrates unsupervised object discovery and segmentation with language input to learn object-centric concepts.
result LORL improves unsupervised object discovery methods and aids downstream tasks.
DADS discovers skills with predictable outcomes from unlabeled data.
problem Learning accurate models for complex dynamical systems is difficult and often doesn't generalize well.
method Dynamics-Aware Discovery of Skills (DADS) combines model-based and model-free learning.
result DADS discovers infinitely many behaviors in high-dimensional state-spaces.
We release a large ECG dataset for arrhythmia subtype discovery.
problem Discovering unknown subtypes of arrhythmia from continuous raw signals.
method Unsupervised representation learning task using semi-supervised evaluation.
result Qualitative evaluations show potential for representation learning in arrhythmia sub-type discovery.
Paper presents a workflow for reliable unsupervised learning in science.
problem Lack of standardization in unsupervised learning workflows for reproducible scientific discoveries.
method Structured workflow including data preparation, modeling, validation, and communication.
result Illustrates the importance of validation in unsupervised learning.
Method uses network biology to construct gene expression models for cancer.
problem Building models for cancer phenotypes using gene expression data.
method Unsupervised construction of computational graphs based on protein-protein networks.
result The method outperforms other models in cancer phenotype analysis.
Paper shows intrinsic motivation boosts exploration efficiency in HRL.
problem Efficient exploration and subgoal discovery in model-free HRL.
method Unsupervised learning over agent's experiences for subgoal discovery.
result Intrinsic motivation learning improves exploration efficiency.
EDL discovers state-covering skills without relying on task rewards.
problem Discovering skills in reinforcement learning without a task-oriented reward function.
method EDL optimizes information-theoretic objective using different machinery to address coverage problem.
result EDL discovers state-covering skills more effectively than existing methods.
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, w…
The paper learns robot skills from demonstrations without supervision.
problem Discovering robotic options from unlabelled demonstrations.
method Temporal variational inference for latent variable learning.
result The framework can learn options across multiple datasets.
Neural topic models improve topic discovery in large corpora.
problem Topic discovery in large text corpora.
method Neural auto-encoder based document embedding with PLSA assumptions.
result Neural topic models outperform LDA in capturing relevant topics and perplexity.
Paper uses knowledge bases to discover new relations from text.
problem Discover new relations from text without annotated data.
method Construct constraints based on knowledge base embeddings and incorporate into variational auto-encoder for relation discovery.
result Improves relation discovery performance significantly.
A model learns causal representations from high-dimensional data.
problem Challenges in learning causal representations from high-dimensional data.
method Formulated a latent variable decoder model, Decoder BCD, for Bayesian causal discovery.
result Shows that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters.
SMILES Transformer learns molecular fingerprints for drug discovery.
problem Poor performance of rule-based molecular fingerprints in shallow prediction models or small datasets.
method Unsupervised pre-training of a sequence-to-sequence language model on a corpus of SMILES.
result SMILES Transformer outperformed existing methods in small-data settings.
Slot Attention extracts object-centric representations from images.
problem Learning distributed representations that don't capture natural scene composition.
method Slot Attention module interfaces with CNN outputs to produce task-dependent abstract slots.
result Slot Attention enables generalization to unseen compositions.
This paper introduces a new system for discovering patterns in morphogenetic systems using modular architecture and unsupervised learning.
problem Discovering novel patterns in morphogenetic systems is challenging and often relies on manual tuning.
method Introduces a hierarchical, modular architecture for unsupervised learning of diverse representations combined with goal exploration algorithms.
result The new system efficiently adapts diversity search towards user preferences with minimal feedback.
Framework discovers sub-goals for better exploration in RL tasks.
problem Improving exploration in sequential decision making under partial observability.
method Variational intrinsic control framework with information theoretic regularizer.
result Sub-goals discovered without explicit supervision lead to better exploration and sample efficiency.
Unsupervised learning of speech representations using WaveNet autoencoders.
problem Extract meaningful latent representations of speech signals.
method Applying autoencoding neural networks to speech waveforms, using a high capacity WaveNet decoder, and comparing three variants of latent representations.
result Comparable performance with top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.
TokenCut detects and segments objects in images and videos without supervision.
problem Detecting and segmenting salient objects in images and videos without labeled data.
method Graph-based approach using self-supervised transformer features and Normalized Cut algorithm.
result Achieves state-of-the-art results on various detection and segmentation tasks.
The paper shows how continuous attention can lead to better object representations.
problem Learning good representations from data without labeled examples.
method Integrating unsupervised learning and reinforcement learning with intrinsic motivation.
result The proposed algorithm improves object recognition in few-shot settings.
An unsupervised neural network learns event truths from social network data.
problem Estimating event truths from conflicting opinions in social networks.
method Autoencoder learns relationships, Bayesian network models agent reliability and social relationships, variational inference estimates hidden variables and parameters.
result The approach outperforms state-of-the-art methods on real datasets.
Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any lin…
LANCA uses ANM to learn latent causal factors without supervision.
problem Learning latent causal factors without supervision.
method LANCA employs a deterministic Wasserstein Auto-Encoder coupled with a differentiable ANM Layer.
result LANCA outperforms baselines on physics and photorealistic environments.
Bayesian model enhances phenotype discovery in asthma EHRs.
problem Lack of interpretability in unsupervised learning phenotyping of EHR data.
method Operationalized a Bayesian latent class framework with clinical knowledge priors.
result Identified an asthma sub-phenotype with elevated eosinophil levels and allergy markers.
Improved training for VQ-VAE models with robust codebook learning.
problem Challenges in training discrete latent variable models, especially VQ-VAEs.
method Increased learning rate and periodic re-initialization of codebook for robust training.
result More robust training and increased usage of latent codewords, even for large codebooks.
Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a…
UCSL combines clustering with supervised learning to discover interpretable subtypes.
problem Discovering interpretable subtypes in datasets relevant to supervised tasks.
method UCSL (Unsupervised Clustering driven by Supervised Learning) framework integrating clustering and supervised learning.
result UCSL achieves +1.9 points in balanced accuracy for psychiatric diseases clustering.
We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.
problem Noise and interval censoring hinder clustering in disease phenotyping.
method Deep generative, continuous-time model that clusters time-series data while correcting for censorship.
result Our model corrects for interval censoring and recovers known clinical subtypes.
Framework detects anomalies in industrial processes using deep learning.
problem Detect anomalies in complex industrial processes.
method Causal-based framework with unsupervised deep learning.
result Successfully validated abstract contexts of blast furnace assets.
New method uses probabilistic independence to discover disease signatures from medical records.
problem Insufficiently precise diagnosis of clinical disease leading to treatment failures.
method Unsupervised machine learning using probabilistic independence to disentangle disease patterns.
result Inferred 2000 clinical disease signatures from medical records, improving cancer prediction.
Automatically learns dynamical distances for efficient reinforcement learning.
problem Difficult to specify reward functions for reinforcement learning.
method Uses dynamical distances to provide well-shaped reward functions.
result Can learn complex tasks efficiently with minimal supervision.
uGLAD recovers sparse graphs from data using deep unrolled networks.
problem Sparse graph recovery from complex data.
method Optimizing deep unrolled networks to learn precision matrices.
result uGLAD outperforms existing algorithms in sparsity optimization and robustly handles missing data.
Unsupervised method discovers interpretable directions in GAN latent space.
problem Discovering interpretable directions in GAN latent space without supervision.
method Model-agnostic procedure to identify directions corresponding to semantic manipulations.
result Findings include directions for background removal and competitive saliency detection performance.
DeepFDR uses deep learning for better FDR control in neuroimaging data.
problem Spatial dependence among voxel-based tests in neuroimaging data.
method DeepFDR leverages unsupervised deep learning-based image segmentation.
result DeepFDR outperforms existing methods in FDR control and computational efficiency.
Transformer-based method discovers objects from images without labels.
problem Discovering objects in images without labeled data.
method Graph-based approach using self-supervised transformer features and normalized graph-cut.
result Significantly boosts performance in unsupervised object discovery.
Paper proposes a new dataset for group anomaly detection in physics.
problem Group anomaly detection in physics.
method Development of a synthetic benchmark dataset and comparison of anomaly detection techniques.
result Demonstrated performance of anomaly detection techniques on the new dataset.
Unsupervised method selects genes for tumor subtype discovery.
problem High-dimensional tumor gene expression data with noisy variables and heterogeneity.
method Autoencoders for latent space learning, Multiple Kernel Learning for feature selection, clustering.
result Lower redundancy and better clustering performance compared to benchmarks.
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an o…
New model separates images into independent factors quickly and easily.
problem Separating high-dimensional data like images into independent latent factors.
method Combines bijective feature maps with linear ICA model on the Stiefel manifold.
result Models converge quickly and achieve better unsupervised latent factor discovery.
The paper investigates how irrelevant features affect clustering performance.
problem The challenge of identifying relevant features in unsupervised clustering tasks.
method Investigation of clustering performance with added irrelevant features.
result Different types of irrelevant features impact clustering outcomes differently.
Paper introduces SUEL model for integrating predictors without labeled data.
problem Combining predictors with unknown accuracy and high correlation.
method Structured unsupervised ensemble learning (SUEL) with correlation-based decomposition algorithms.
result Efficient integration of dependent predictors without labeled data.
DBULL learns new clusters without forgetting past knowledge in streaming unlabelled data.
problem Challenges in Unsupervised Lifelong Learning with evolving data distributions and class labels.
method Bayesian framework for incremental learning, Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, knowledge preservation mechanism, automatic cluster discovery.
result DBULL can progressively discover new clusters without forgetting past knowledge in unlabelled data.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
Interpretable ML helps discover insights from big data.
problem Validating data-driven discoveries from complex datasets.
method Statistical and machine learning techniques for interpretable models.
result Challenges in validating data-driven discoveries remain.
New deep generative model improves semi-unsupervised classification.
problem Learning from datasets with sparse labels.
method Gaussian mixture deep generative model for semi-unsupervised classification.
result Superior performance on MNIST and human activity classification.
Neural Shadow-Mapping uncovers causal links in dynamic systems.
problem Discovering causal structures in dynamic systems with mirage correlations.
method Neural network based method embedding high-dimensional data into a shadow representation for causal link estimation.
result Demonstrates performance in discovering causal links from video-representations of dynamic systems.
Unsupervised meta-learning improves learning from small labeled data.
problem Acquiring representations from unlabeled data for effective downstream learning.
method Develops an unsupervised meta-learning method that optimizes for task learning ability from unlabeled data.
result Simple task construction mechanisms, like clustering embeddings, lead to good performance on various downstream tasks.
The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover…