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Found 23 papers, 3 papers with code
DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
Locally Distributed Activation Vectors for Guided Feature Attribution
Explaining the predictions of a deep neural network (DNN) is a challenging problem.
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles
In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles.
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices.
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment.
Explaining Autonomous Driving Actions with Visual Question Answering
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms.
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension.
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
Deep Temporal Modelling of Clinical Depression through Social Media Text
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts.
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
In our work, we describe a Semi-supervised Learning (SSL) framework which uses an initial supervised learning model that leverages 1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated Depression Tweets Repository (DTR).
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space.
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving.
Towards Safe, Explainable, and Regulated Autonomous Driving
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning.
Autonomous Driving
Explainable Artificial Intelligence (XAI)
+1
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned from a much smaller and more specific domain dataset through a simple non-linear mapping mechanism.
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i. e. Depression Symptoms Detection (DSD) from text.
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.
DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector
Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret.
RANCC: Rationalizing Neural Networks via Concept Clustering
We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks.
On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering
Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures.
An Introduction to Deep Visual Explanation
The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem.
Using KL-divergence to focus Deep Visual Explanation
We present a method for explaining the image classification predictions of deep convolution neural networks, by highlighting the pixels in the image which influence the final class prediction.
Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis
However, our abduction algorithm is exponential only in the number of observations to be explained, and is polynomial in the size of the knowledge base.
