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Found 31 papers, 5 papers with code
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
Recent advances in fine-tuning large language models (LLMs) with reinforcement learning (RL) have shown promising improvements in complex reasoning tasks, particularly when paired with chain-of-thought (CoT) prompting.
Knowledge Consultation for Semi-Supervised Semantic Segmentation
Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance.
MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
Data-driven methods such as deep neural networks make no such assumptions and can capture the generative process in more detail, but fail in long-term forecasting due to data limitations.
Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning
To this end, we leverage the Hadamard product for calibrating and updating memory, specifically designed to enhance memory capacity while mitigating numerical and learning challenges.
Contrastive explainable clustering with differential privacy
In each contrastive scenario, we designate a specific data point as the fixed centroid position, enabling us to measure the impact of this constraint on clustering utility under differential privacy.
Differentially private exact recovery for stochastic block models
A fundamental problem in SBMs is the recovery of the community structure, and sharp information-theoretic bounds are known for recoverability for many versions of SBMs.
Multi-Reference Preference Optimization for Large Language Models
Recent approaches, such as direct preference optimization (DPO), have eliminated the need for unstable and sluggish reinforcement learning optimization by introducing close-formed supervised losses.
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data.
Revisiting the Dataset Bias Problem from a Statistical Perspective
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
Variational Flow Models: Flowing in Your Style
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+\sigma_{t}X_{1} into a straight constant-speed (SC) flow, reminiscent of Rectified Flow.
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges.
Cloth-Changing Person Re-Identification
Contrastive Learning
+1
Beyond Surprise: Improving Exploration Through Surprise Novelty
We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations.
Memory-Augmented Theory of Mind Network
Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cognitive structure.
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head.
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.
Differentially Private Partial Set Cover with Applications to Facility Location
Using our algorithm for Partial Set Cover as a subroutine, we give a differentially private (bicriteria) approximation algorithm for a facility location problem which generalizes $k$-center/$k$-supplier with outliers.
Learning to Constrain Policy Optimization with Virtual Trust Region
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update.
Learning to Transfer Role Assignment Across Team Sizes
In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment.
Learning Theory of Mind via Dynamic Traits Attribution
Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories.
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious.
Differentially Private Community Detection for Stochastic Block Models
To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection.
Etude de classification des bacteriophages
Phages are one of the most present groups of organisms in the biosphere.
Episodic Policy Gradient Training
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.
Differentially Private Densest Subgraph Detection
We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private.
Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets
A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses.
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning
In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM).
Meta Transfer Learning for Emotion Recognition
To mitigate this challenge, transfer learning performing fine-tuning on pre-trained models has been applied.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area.
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition
Deep learning has been applied to achieve significant progress in emotion recognition.
Learning to Attend Relevant Regions in Videos from Eye Fixations
Attentively important regions in video frames account for a majority part of the semantics in each frame.
Meta Transfer Learning for Facial Emotion Recognition
The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning.
