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Found 27 papers, 12 papers with code
Contrastive Learning of Sentence Representations
Learning sentence representations which capture rich semantic meanings has been crucial for many NLP tasks.
Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective
Large Language Models (LLMs) are central to a multitude of applications but struggle with significant risks, notably in generating harmful content and biases.
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.
Self-Supervised Object Goal Navigation with In-Situ Finetuning
In contrast, our LocCon shows the most robust transfer in the real world among the set of models we compare to, and that the real-world performance of all models can be further improved with self-supervised LocCon in-situ training.
Generalized Category Discovery with Decoupled Prototypical Network
Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance.
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set.
ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks
With event-driven algorithms, especially the spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream-dataset is urgently needed.
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks.
Learning a Proposal Classifier for Multiple Object Tracking
In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph.
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze
It is a common practice for vehicles to use GPS to acquire location information.
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion.
Ranked #11 on
Gesture Recognition
on DVS128 Gesture
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance
To overcome this dilemma, we take a different perspective and investigate a new approach to handle inaccuracy and uncertainty in the training data labels, which are inevitable in many applications where labels are conceptual and labeling is performed by human annotators.
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis
Summarization conducted by the \textit{SummerTime} method will be a fixed-length feature vector which can be used in-place of the time series dataset for use with classical machine learning methods.
CFS: A Distributed File System for Large Scale Container Platforms
We propose CFS, a distributed file system for large scale container platforms.
Distributed, Parallel, and Cluster Computing
Measuring similarity between geo-tagged videos using largest common view
In this paper, we propose new algorithm that can group both spatial locations and points of view to identify similar trajectories.
Privacy and Utility Tradeoff in Approximate Differential Privacy
We show that the multiplicative gap of the lower bounds and upper bounds goes to zero in various high privacy regimes, proving the tightness of the lower and upper bounds and thus establishing the optimality of the truncated Laplacian mechanism.
Optimal Noise-Adding Mechanism in Additive Differential Privacy
We derive the optimal $(0, \delta)$-differentially private query-output independent noise-adding mechanism for single real-valued query function under a general cost-minimization framework.
A Semantic QA-Based Approach for Text Summarization Evaluation
In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books).
Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation
For this, our approach utilizes a mathematical model originating in natural language processing, the bag-of-words model, that has in the past years been appearing in diverse disciplines outside of the natural language processing field such as image processing.
Scale Normalization
One of the difficulties of training deep neural networks is caused by improper scaling between layers.
Rapid building detection using machine learning
This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available.
LOFS: Library of Online Streaming Feature Selection
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed.
RandomOut: Using a convolutional gradient norm to rescue convolutional filters
We propose to evaluate and replace specific convolutional filters that have little impact on the prediction.
Crater Detection via Convolutional Neural Networks
The power of CNNs is that they can learn image filters which generate features for high accuracy classification.
Scalable and Accurate Online Feature Selection for Big Data
Feature selection is important in many big data applications.
A Common-Factor Approach for Multivariate Data Cleaning with an Application to Mars Phoenix Mission Data
Data quality is fundamentally important to ensure the reliability of data for stakeholders to make decisions.
XTreePath: A generalization of XPath to handle real world structural variation
We propose the XTreePath annotation method to captures contextual node information from the training DOM.
