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Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

- Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing methods face a critical...

Fully Dynamic Graph Algorithms with Edge Differential Privacy

- We study differentially private algorithms for analyzing graphs in the challenging setting of continual release with fully dynamic updates, where edges are inserted and deleted over time, and the...

Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy

- In this paper, we establish a novel connection between the metric entropy growth and the embeddability of function spaces into reproducing kernel Hilbert/Banach spaces. Metric entropy characterizes...

A Systematic Literature Review on the NIS2 Directive

- The second network and information security (NIS2) directive was enacted in the European Union (EU) in late 2022. It deals particularly with European critical infrastructures, enlarging their scope...

Leader Rotation Is Not Enough: Scrutinizing Leadership Democracy of Chained BFT Consensus

- With the growing popularity of blockchains, modern chained BFT protocols combining chaining and leader rotation to obtain better efficiency and leadership democracy have received increasing interest....

Unraveling Responsiveness of Chained BFT Consensus with Network Delay

- With the advancement of blockchain technology, chained Byzantine Fault Tolerant (BFT) protocols have been increasingly adopted in practical systems, making their performance a crucial aspect of the...

Impatient Bandits: Optimizing for the Long-Term Without Delay

- Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a bandit problem with...

Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions

- Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contamination....

One-Bit Distributed Mean Estimation with Unknown Variance

- In this work, we study the problem of distributed mean estimation with 1-bit communication constraints when the variance is unknown. We focus on the setting where each user has access to one iid...

FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction

- Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents significant challenges due...

Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in Non-Stationary Environments

- Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still...

Compositional Verification of Concurrency Using Past-Time Temporal Epistemic Logic

- Shared-memory concurrency is notoriously difficult to reason about because each thread executes under interference from other threads. At the same time, many correctness arguments for classical...

Societal Alignment Frameworks Can Improve LLM Alignment

- Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs...

Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark

- Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary data used for open-vocabulary learning can lead to...

Experiments with Optimal Model Trees

- Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems. In contrast to ``classic'' decision trees with constant values...

Ensemble Learning for Large Language Models in Text and Code Generation: A Survey

- Generative Pretrained Transformers (GPTs) are foundational Large Language Models (LLMs) for text generation. However, individual LLMs often produce inconsistent outputs and exhibit biases, limiting...

XConv: Low-memory stochastic backpropagation for convolutional layers

- Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation. Existing remedies (checkpointing, invertible...

EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

- We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods,...

Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition

- Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as...

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

- Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data. However, it remains an open question how deep networks perform...

Evaluation Metrics as Averaged Outcomes of Fair Gambles

- In the current practices of machine learning, the evaluation of forecasts has become a cornerstone of scientific progress. A multitude of evaluation metrics have been suggested and used to qualify...