Paper proposes a new speech representation benchmark and model.
problem Lack of benchmarks for comparing speech representations.
method Unsupervised triplet-loss objective for training a universal non-semantic speech representation.
result Proposed representation outperforms other models on benchmark and transfer learning tasks.
VTAB benchmarks diverse visual tasks to assess representation learning effectiveness.
problem Lack of a unified evaluation for general visual representations.
method Developed VTAB, a benchmark for diverse visual tasks, and evaluated many representation learning algorithms.
result VTAB revealed insights into the effectiveness of various representation learning methods.
Method learns state representations without supervision for Atari games.
problem Learning state representations without supervision.
method Maximizes mutual information across features of neural encoder.
result New benchmark for evaluating representation learning models.
URL benchmark evaluates uncertainty quantification in pretrained models.
problem Need for reliable uncertainty estimates in transferable pretrained models.
method Proposes URL benchmark to measure transferability of representations and uncertainty estimates.
result Transferable uncertainty quantification remains challenging but not contradictory to traditional goals.
New approach learns graph representations by contrasting first-order neighbors and graph diffusion views.
problem Learning node and graph level representations from graph data.
method Self-supervised approach using contrastive learning of multi-scale encodings.
result Achieves state-of-the-art performance on 8 out of 8 benchmarks.
BGRL learns graph representations without costly negative examples.
problem Efficient representation learning on large graphs without labels.
method Bootstrapped Graph Latents (BGRL) learns by predicting augmentations.
result BGRL achieves state-of-the-art performance with 2-10x memory savings.
New method computes optimal fairness-performance trade-off without complex models.
problem Intrinsic trade-off between fairness and classifier performance.
method Computes optimal Pareto front without training complex models.
result Optimal fair representations have useful structural properties enabling efficient computation.
CausalCOMRL improves RL task representations by integrating causal relationships, enhancing generalizability.
problem Spurious correlations in context-based offline meta-reinforcement learning.
method CausalCOMRL integrates causal representation learning to uncover and incorporate causal relationships among task components.
result CausalCOMRL achieves better performance on meta-reinforcement learning benchmarks.
HEAR benchmark evaluates audio representations for diverse tasks.
problem Developing a general-purpose audio representation for various tasks.
method Evaluated 29 models across 19 tasks using 16 datasets.
result No single audio representation performs holistically.
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard b…
SEMASIA provides a large dataset of latent representations for model comparison.
problem Difficulty in comparing semantic structures across different neural network models.
method Collection of latent representations from 1700 pretrained models across various benchmarks.
result Consistent semantic organization across models and datasets.
Paper introduces SALE for better state-action learning in RL.
problem Challenges in representation learning for low-level states in RL.
method Introduces SALE, a novel approach for learning embeddings of state-action interactions.
result TD7 algorithm significantly outperforms existing continuous control algorithms.
CDSSL improves representation quality by integrating linear and nonlinear dependencies.
problem Scarcity of labeled data and neglect of nonlinear dependencies in SSL.
method CDSSL combines linear correlations and nonlinear dependencies using HSIC in RKHS.
result CDSSL enhances representation quality on diverse benchmarks.
Proposes a new neural head for asymmetric representation learning.
problem Asymmetric representation learning in directed relations.
method Role-aware neural convex divergence head.
result Role-aware projections improve directional accuracy over plain ICNN-Bregman heads.
DGE learns event representations from image sequences without manual annotations.
problem Data hunger and domain adaptation issues in self-supervised learning for temporal segmentation.
method Dynamic Graph Embedding (DGE) learns event representations by iteratively updating a graph and its embedding.
result DGE achieves robust temporal segmentation on benchmark datasets, outperforming state-of-the-art methods.
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
The study uncovers latent capabilities of language models via causal representation learning.
problem Rigorous causal evaluations of language model capabilities are challenging due to confounding effects and computational costs.
method Proposes a causal representation learning framework to identify latent capability factors as causally interrelated after controlling for a common confounder (base model).
result Identifies a three-node linear causal structure explaining performance variations across 1500 models and six benchmarks.
GraphCL learns node representations by maximizing similarity between perturbed node features.
problem Learning node representations in graph data without labeled data.
method Contrastive learning of node embeddings using graph neural networks and a loss function.
result Significantly outperforms state-of-the-art in unsupervised node classification benchmarks.
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchica…
New task aligns molecular structure with gene expression changes.
problem Modeling the relationship between chemical structure and gene expression changes.
method Developed a cross-modal small molecule retrieval task and a coordinated deep learning approach to align chemical structure and gene expression profiles.
result Demonstrated the feasibility of the new task and highlighted the limitations of current data and systems.
CaGAT learns context-aware edge representations for graph data.
problem Ignoring edge representation in GNNs.
method Unified Context-aware Adaptive Graph Attention Network (CaGAT) that learns both node and edge representations.
result CaGAT improves performance on semi-supervised learning tasks.
MetaFun learns functional representations for meta-learning.
problem Few-shot classification on large-scale datasets.
method Functional encoder-decoder approach with iterative updates.
result State-of-the-art performance on miniImageNet and tieredImageNet.
Model learns disentangled representations from natural videos.
problem Disentangling factors of variation in natural data.
method Sparse prior on temporally adjacent observations.
result Model reliably learns disentangled representations on natural data.
Benchmark tests spoken language models for infant language learning.
problem Understanding how infants learn language from speech.
method Developed a language-acquisition-friendly benchmark.
result Benchmarking shows models' strengths and weaknesses.
DHOG improves unsupervised clustering accuracy on image benchmarks.
problem Local optima in mutual information maximisation lead to suboptimal representations.
method Deep hierarchical object grouping (DHOG) computes multiple discrete representations in a hierarchical order.
result DHOG achieves new state-of-the-art results on three main benchmarks.
Hyperbolic GNNs improve graph data learning.
problem Learning from graph-structured data.
method Proposes a novel GNN architecture for Riemannian manifolds.
result Hyperbolic GNNs lead to substantial improvements on benchmark datasets.
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is link…
We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free). Rather than fixing the class prototypes to be the Euclidean average of sample embeddings, we allow them to live in a higher-dimensional space (embedded class models) an…
Deep GNNs and self-supervision boost graph learning at scale.
problem Efficiently deploying GNNs at large scale remains challenging.
method Two large-scale GNNs: a deep transductive node classifier and a very deep inductive graph regressor.
result Award-level performance on MAG240M and PCQM4M benchmarks.
Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments, then benchmarking algorithms and model architectures on those environments. We discuss subtle conceptual properties of RL benchmarks that are not required in su…
New method pools graphs with edge features for molecular data.
problem Pooling graphs with edge features for molecular data.
method Proposes two types of pooling layers compatible with edge-feature graph-convolutional architecture.
result Significantly outperforms previous benchmarks on three out of four MoleculeNet datasets.
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link pr…
IdBench benchmarks semantic representations of identifiers, revealing strengths and weaknesses.
problem Evaluating semantic representations of identifiers in source code.
method Created a benchmark using developer ratings, evaluated natural language and source code embeddings, and compared lexical string distance functions.
result No single technique provides a satisfactory representation of semantic similarities, but ensemble models can improve performance.
Proposes a deep learning method for effective data representation.
problem Constructing effective data representations for prediction.
method A deep dimension reduction approach to learning representations with sufficiency, low dimensionality, and disentanglement.
result The proposed deep nonparametric representation is consistent and performs better than existing methods.
We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link…
We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast wei…
QGNN uses Quaternion space for better graph and node classification.
problem Existing GNN methods struggle with Euclidean vector space limitations.
method Proposes QGNN to learn graph representations in Quaternion space.
result Obtains state-of-the-art results on graph and node classification benchmarks.
We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor…
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
problem Difficulty in transferring results across publications due to targeted algorithmic data.
method Develops a comprehensive benchmark covering various algorithmic tasks.
result Demonstrates performance of algorithmic reasoning baselines on the CLRS benchmark.
Proposes LMSSC for multi-view semi-supervised classification.
problem Leveraging multiple complementary views for improved classification.
method Semi-supervised classification with latent multi-view representation learning.
result Unified framework for latent representation learning, graph construction, and label propagation.
This work defines idealized SSL representations and improves existing methods.
problem Unclear characteristics of SSL representations leading to high downstream accuracies.
method Characterized ideal properties and derived necessary and sufficient conditions.
result Improved SSL methods and derived new objectives for contrastive and non-contrastive learning.
A neural scene representation framework enforcing 3D transformations.
problem Learning 3D scene representations from images without 3D supervision.
method Introducing a loss enforcing equivariance of the scene representation with 3D transformations.
result Real-time neural rendering with comparable results to models requiring minutes for inference.
IsUMap improves data visualization of complex geometries.
problem Accurately representing complex, locally distorted metric spaces.
method Integrates UMAP and Isomap with Vietoris-Rips filtrations.
result Significant improvements in data representation quality.
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
This work shows how disentangled and sparse representations improve multi-task learning.
problem Improving generalization in multi-task learning with disentangled and sparse representations.
method Proved a new identifiability result and proposed a practical approach using sparsity-promoting bi-level optimization.
result Maximally sparse base-predictors yield disentangled representations under certain conditions.
New method learns useful disentangled representations from weakly labeled data.
problem Learning useful representations from weakly labeled data.
method Model pairs of non-i.i.d. images, learn disentangled representations without requiring annotation.
result Learn disentangled representations reliably from pairs of images without requiring group, individual factor, or number of changed factors annotation.