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Found 16 papers, 13 papers with code
TOOLVERIFIER: Generalization to New Tools via Self-Verification
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem.
A Data Source for Reasoning Embodied Agents
In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent.
General Multi-label Image Classification with Transformers
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image.
Reevaluating Adversarial Examples in Natural Language
State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack.
Neural Message Passing for Multi-Label Classification
We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels.
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Statistical language models are powerful tools which have been used for many tasks within natural language processing.
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios.
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms.
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation.
GaKCo: a Fast GApped k-mer string Kernel using COunting
This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to $O(\Sigma^{M})$.
Memory Matching Networks for Genomic Sequence Classification
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs".
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context.
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification.
DeepChrome: Deep-learning for predicting gene expression from histone modifications
To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model.
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics.
Deep Motif: Visualizing Genomic Sequence Classifications
This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task.
