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Found 24 papers, 6 papers with code
Set the Clock: Temporal Alignment of Pretrained Language Models
We then develop several methods, from prompting to finetuning, to align LMs to use their most recent knowledge when answering questions, and investigate various factors in this alignment.
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
Compared to baselines, our experiments show that APT maintains up to 98% task performance when pruning RoBERTa and T5 models with 40% parameters left while keeping 86. 4% LLaMA models' performance with 70% parameters remained.
Large Language Models are Complex Table Parsers
With the Generative Pre-trained Transformer 3. 5 (GPT-3. 5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering (QA) research has primarily centered around general QA tasks based on GPT, neglecting the specific challenges posed by Complex Table QA.
Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation
To tackle the problem of efficiently editing neural implicit fields, we introduce Neural Impostor, a hybrid representation incorporating an explicit tetrahedral mesh alongside a multigrid implicit field designated for each tetrahedron within the explicit mesh.
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization
Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization.
When Evolutionary Computation Meets Privacy
To this end, in this paper, we discuss three typical optimization paradigms (i. e., \textit{centralized optimization, distributed optimization, and data-driven optimization}) to characterize optimization modes of evolutionary computation and propose BOOM to sort out privacy concerns in evolutionary computation.
GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners
Automatic unknown word detection techniques can enable new applications for assisting English as a Second Language (ESL) learners, thus improving their reading experiences.
A Survey of Secure Computation Using Trusted Execution Environments
To the best of our knowledge, this article is the first survey to review TEE-based secure computation protocols and the comprehensive comparison can serve as a guideline for selecting suitable protocols for deployment in practice.
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias
For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples.
DELTA: degradation-free fully test-time adaptation
However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning.
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph.
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid.
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image.
Rethinking Attention Mechanism in Time Series Classification
Attention-based models have been widely used in many areas, such as computer vision and natural language processing.
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization
Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner.
On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach
Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution.
Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing
This paper adapts the evolutionary multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO problem.
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification
For each connected user, its student model's hidden layers' weights are uploaded to the EFDLS server periodically.
Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment
For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples.
Energy Aligning for Biased Models
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem.
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System
Specifically, in order to protect privacy, participants locally process sensing data via federated learning and only upload encrypted training models.
Towards a category-extended object detector with limited data
Object detectors are typically learned on fully-annotated training data with fixed predefined categories.
Maintaining Discrimination and Fairness in Class Incremental Learning
In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes.
Ranked #2 on
Incremental Learning
on ImageNet100 - 10 steps
(# M Params metric)
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).
