arXiv research

Agent learns to solve unseen tasks with subtask dependencies.

Meta-learner infers subtask graph to adapt quickly to unknown tasks.

HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.

Task focuses on fact checking in Q&A forums, improving over baseline systems.

RoBERTa model detects counterfactual statements in text.

Deep network improves NILM by distinguishing on/off states of appliances.

In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of ar…

MAML optimizes shared priors for subtasks in a nonconvex meta-objective.

PALM learns abstract models for efficient planning and task transfer.

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise an…

New algorithm trains neural nets on simple skills to learn complex tasks faster.

PLOTS learns procedural actions from observed sequences, up to 100x faster.

Unified pair trading approach using hierarchical reinforcement learning.

This paper evaluates how well neural models can solve complex tasks by breaking them into simpler ones.

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to sever…

Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue…

Hotel booking chatbot handles daily tens of thousands of searches.

The paper proposes methods to estimate positive examples and learn classifiers from mixed data.

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural…

In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fin…

Ranked second in fact-checking task, using DRR NN with embeddings.

Survey on deep learning methods for stock market prediction.

Polish hate speech detection team second in PolEval 2019.

AI solutions matched prosthetic control goals in a challenge.

Neural networks learn more efficiently with hidden factorial structures.

Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even to similar domains. To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, s…

Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, players may grow tired of existing charts, or wish to dance to a song for which no …

Proposes LMSSC for multi-view semi-supervised classification.

Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times…

AxCell automates results extraction from machine learning papers.

MGHRL learns to generate high-level meta strategies for new tasks.

System classifies Twitter and Reddit posts' stance towards hidden rumour threads.

A new model improves recurrent neural networks' ability to memorize long sequences.

The paper tackles class imbalance in deep learning models and proposes a method to enhance feature extraction.

Transformers can generalize to a large task family with only a few demonstrations.

Proposes CORAL framework for consistent ordinal regression in neural networks.

TIM framework uses LLMs and domain experts to infer DeFi user transaction intents.

Generative models learn from unlabeled videos via object segmentation and scene modeling.

Method learns multi-stage tasks from single video, overcoming challenges of raw pixel learning and insufficient demonstrations.

With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistic…

Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemb…

A new training method improves stability and generalization of DeepONets.

Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several co…

Paper speeds up DGD by allowing multiple transmissions from non-straggling servers.

Director learns hierarchical behaviors from pixels, outperforming exploration methods.

AgensFlow learns multi-agent coordination policies from experience.

Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.

New method corrects MRI biases across scanners and sites.