bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
Authors: Park Junseok , Kim Yoonsung , Yoo Hee bin , Lee Min Whoo , Kim Kibeom , Choi Won-Seok , Lee Minsu , Zhang Byoung-Tak
Published: March 2024

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Authors: Azran Guy , Danesh Mohamad H. , Albrecht Stefano V. , Keren Sarah
Published: March 2024

Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward machines (RMs), state machine abstractions that induce subtasks based on the current task’s rewards and dynamics. Our method provides agents with symbolic representations of optimal transitions from their current abstract state and rewards them for achieving these transitions. These representations are shared across tasks, allowing agents to exploit knowledge of previously encountered symbols and transitions, thus enhancing transfer. Empirical results show that our representations improve sample efficiency and few-shot transfer in a variety of domains.
Maximizing the Success Probability of Policy Allocations in Online Systems
Authors: Betlei Artem , Vladimirova Mariia , Sebbar Mehdi , Urien Nicolas , Rahier Thibaud , Heymann Benjamin
Published: March 2024

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the specific context of online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.
HyperFast: Instant Classification for Tabular Data
Authors: Bonet David , Mas Montserrat Daniel , Giró-i-Nieto Xavier , Ioannidis Alexander G.
Published: March 2024

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning. Our code, which offers a scikit-learn-like interface, along with the trained HyperFast model, can be found at https://github.com/AI-sandbox/HyperFast.
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions
Authors: Chan T-H. Hubert , Xie Hao , Zhao Mengshi
Published: March 2024

We examine a private ADMM variant for (strongly) convex objectives which is a primal-dual iterative method. Each iteration has a user with a private function used to update the primal variable, masked by Gaussian noise for local privacy, without directly adding noise to the dual variable. Privacy amplification by iteration explores if noises from later iterations can enhance the privacy guarantee when releasing final variables after the last iteration.

Cyffers et al. explored privacy amplification by iteration for the proximal ADMM variant, where a user's entire private function is accessed and noise is added to the primal variable. In contrast, we examine a private ADMM variant requiring just one gradient access to a user's function, but both primal and dual variables must be passed between successive iterations.

To apply Balle et al.'s coupling framework to the gradient ADMM variant, we tackle technical challenges with novel ideas. First, we address the non-expansive mapping issue in ADMM iterations by using a customized norm. Second, because the dual variables are not masked with any noise directly, their privacy guarantees are achieved by treating two consecutive noisy ADMM iterations as a Markov operator.

Our main result is that the privacy guarantee for the gradient ADMM variant can be amplified proportionally to the number of iterations. For strongly convex objective functions, this amplification exponentially increases with the number of iterations. These amplification results align with the previously studied special case of stochastic gradient descent.
Offline Model-Based Optimization via Policy-Guided Gradient Search
Authors: Chemingui Yassine , Deshwal Aryan , Hoang Trong Nghia , Doppa Janardhan Rao
Published: March 2024

Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize an unknown function given only its offline evaluation at a fixed set of inputs. A naive solution to this problem is to learn a surrogate model of the unknown function and optimize this surrogate instead. However, such a naive optimizer is prone to erroneous overestimation of the surrogate (possibly due to over-fitting on a biased sample of function evaluation) on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However, their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap, we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.
Focus-Then-Decide: Segmentation-Assisted Reinforcement Learning
Authors: Chen Chao , Xu Jiacheng , Liao Weijian , Ding Hao , Zhang Zongzhang , Yu Yang , Zhao Rui
Published: March 2024

Visual Reinforcement Learning (RL) is a promising approach to achieve human-like intelligence. However, it currently faces challenges in learning efficiently within noisy environments. In contrast, humans can quickly identify task-relevant objects in distraction-filled surroundings by applying previously acquired common knowledge. Recently, foundational models in natural language processing and computer vision have achieved remarkable successes, and the common knowledge within these models can significantly benefit downstream task training. Inspired by these achievements, we aim to incorporate common knowledge from foundational models into visual RL. We propose a novel Focus-Then-Decide (FTD) framework, allowing the agent to make decisions based solely on task-relevant objects. To achieve this, we introduce an attention mechanism to select task-relevant objects from the object set returned by a foundational segmentation model, and only use the task-relevant objects for the subsequent training of the decision module. Additionally, we specifically employed two generic self-supervised objectives to facilitate the rapid learning of this attention mechanism. Experimental results on challenging tasks based on DeepMind Control Suite and Franka Emika Robotics demonstrate that our method can quickly and accurately pinpoint objects of interest in noisy environments. Consequently, it achieves a significant performance improvement over current state-of-the-art algorithms.
Project Page: https://www.lamda.nju.edu.cn/chenc/FTD.html
Code: https://github.com/LAMDA-RL/FTD
On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning
Authors: Chen Jiayi , Zhang Aidong
Published: March 2024

There has been growing concern regarding data privacy during the development and deployment of Multimodal Foundation Models for Artificial General Intelligence (AGI), while Federated Learning (FL) allows multiple clients to collaboratively train models in a privacy-preserving manner. This paper formulates and studies Modality-task Agnostic Federated Learning (AFL) to pave the way toward privacy-preserving AGI. A unique property of AFL is the asymmetrical knowledge relationships among clients due to modality gaps, task gaps, and domain shifts between clients. This raises a challenge in learning an optimal inter-client information-sharing scheme that maximizes positive transfer and minimizes negative transfer for AFL. However, prior FL methods, mostly focusing on symmetrical knowledge transfer, tend to exhibit insufficient positive transfer and fail to fully avoid negative transfer during inter-client collaboration. To address this issue, we propose DisentAFL, which leverages a two-stage Knowledge Disentanglement and Gating mechanism to explicitly decompose the original asymmetrical inter-client information-sharing scheme into several independent symmetrical inter-client information-sharing schemes, each of which corresponds to certain semantic knowledge type learned from the local tasks. Experimental results demonstrate the superiority of our method on AFL than baselines.
Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Authors: Chen Mulin , Wang Bocheng , Li Xuelong
Published: March 2024

Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. To handle the general clustering scenario without a prior graph, these models estimate an initial graph beforehand to apply GCN. Throughout the literature, we have witnessed that 1) most models focus on the initial graph while neglecting the original features. Therefore, the discriminability of the learned representation may be corrupted by a low-quality initial graph; 2) the training procedure lacks effective clustering guidance, which may lead to the incorporation of clustering-irrelevant information into the learned graph. To tackle these problems, the Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering. Specifically, we establish a pseudo-siamese network, which incorporates auto-encoder with GCN to emphasize both the graph structure and the original features. On this basis, feature-level contrastive learning is introduced to enhance the discriminative capacity, and the relationship between samples and centroids is employed as the clustering-oriented guidance. Afterward, a two-branch graph learning mechanism is designed to extract the local and global structural relationships, which are further embedded into a unified graph under the cluster-level contrastive guidance. Experimental results on several benchmark datasets demonstrate the superiority of DCGL against state-of-the-art algorithms.
Pushing the Limit of Fine-Tuning for Few-Shot Learning: Where Feature Reusing Meets Cross-Scale Attention
Authors: Chen Ying-Yu , Hsieh Jun-Wei , Li Xin , Chang Ming-Ching
Published: March 2024

Due to the scarcity of training samples, Few-Shot Learning (FSL) poses a significant challenge to capture discriminative object features effectively. The combination of transfer learning and meta-learning has recently been explored by pre-training the backbone features using labeled base data and subsequently fine-tuning the model with target data. However, existing meta-learning methods, which use embedding networks, suffer from scaling limitations when dealing with a few labeled samples, resulting in suboptimal results. Inspired by the latest advances in FSL, we further advance the approach of fine-tuning a pre-trained architecture by a strengthened hierarchical feature representation. The technical contributions of this work include: 1) a hybrid design named Intra-Block Fusion (IBF) to strengthen the extracted features within each convolution block; and 2) a novel Cross-Scale Attention (CSA) module to mitigate the scaling inconsistencies arising from the limited training samples, especially for cross-domain tasks. We conducted comprehensive evaluations on standard benchmarks, including three in-domain tasks (miniImageNet, CIFAR-FS, and FC100), as well as two cross-domain tasks (CDFSL and Meta-Dataset). The results have improved significantly over existing state-of-the-art approaches on all benchmark datasets. In particular, the FSL performance on the in-domain FC100 dataset is more than three points better than the latest PMF (Hu et al. 2022).
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Authors: Chen Yiyue , Vikalo Haris , Wang Chianing
Published: March 2024

Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on aggregating locally trained models rather than collecting clients' potentially private data. In practice, available resources and data distributions vary from one client to another, creating an inherent system heterogeneity that leads to deterioration of the performance of conventional FL algorithms. In this work, we present a federated quantization-based self-supervised learning scheme (Fed-QSSL) designed to address heterogeneity in FL systems. At clients' side, to tackle data heterogeneity we leverage distributed self-supervised learning while utilizing low-bit quantization to satisfy constraints imposed by local infrastructure and limited communication resources. At server's side, Fed-QSSL deploys de-quantization, weighted aggregation and re-quantization, ultimately creating models personalized to both data distribution as well as specific infrastructure of each client's device. We validated the proposed algorithm on real world datasets, demonstrating its efficacy, and theoretically analyzed impact of low-bit training on the convergence and robustness of the learned models.
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Authors: Cheng Yi , Hu Renjun , Ying Haochao , Shi Xing , Wu Jian , Lin Wei
Published: March 2024

Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature interaction is necessary for deep tabular learning. To test this point, we create a synthetic tabular dataset with a mild feature interaction assumption and examine a modified transformer architecture enabling arithmetical feature interactions, referred to as AMFormer. Results show that AMFormer outperforms strong counterparts in fine-grained tabular data modeling, data efficiency in training, and generalization. This is attributed to its parallel additive and multiplicative attention operators and prompt-based optimization, which facilitate the separation of tabular samples in an extended space with arithmetically-engineered features. Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data. Code is available at https://github.com/aigc-apps/AMFormer.
Deletion-Robust Submodular Maximization with Knapsack Constraints
Authors: Cui Shuang , Han Kai , Huang He
Published: March 2024

Submodular maximization algorithms have found wide applications in various fields such as data summarization, recommendation systems, and active learning. In recent years, deletion-robust submodular maximization algorithms have garnered attention due to their significant implications in scenarios where some data points may be removed due to user preferences or privacy concerns, such as in recommendation systems and influence maximization. In this paper, we study the fundamental problem of submodular maximization with knapsack constraints and propose a robust streaming algorithm for it. To the best of our knowledge, our algorithm is the first to solve this problem for non-monotone submodular functions and can achieve an approximation ratio of 1/(6.82+2.63d)-ϵ under a near-optimal summary size of O(k+r), where k denotes the maximum cardinality of any feasible solution, d denotes the number of the knapsack constraints and r is the robustness parameter. For monotone submodular functions, our algorithm can achieve an approximation ratio of 1/(2+2d)-ϵ under a near-optimal summary size of O(k+r), significantly improving upon the best-known ratio of Ω((1/d-ϵ)^2). The empirical performance of our algorithm is extensively evaluated in several applications including influence maximization and recommendation systems, and the experimental results demonstrate the effectiveness of our algorithm.
Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video Generation
Authors: Davtyan Aram , Favaro Paolo
Published: March 2024

We propose a novel unsupervised method to autoregressively generate videos from a single frame and a sparse motion input. Our trained model can generate unseen realistic object-to-object interactions. Although our model has never been given the explicit segmentation and motion of each object in the scene during training, it is able to implicitly separate their dynamics and extents. Key components in our method are the randomized conditioning scheme, the encoding of the input motion control, and the randomized and sparse sampling to enable generalization to out of distribution but realistic correlations. Our model, which we call YODA, has therefore the ability to move objects without physically touching them. Through extensive qualitative and quantitative evaluations on several datasets, we show that YODA is on par with or better than state of the art video generation prior work in terms of both controllability and video quality.
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
Authors: De Vazelhes William , Mukhoty Bhaskar , Yuan Xiao-Tong , Gu Bin
Published: March 2024

Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through early stopping, rather than the tedious grid-search used in the traditional methods.
However, most of those iterative methods are based on the l1 norm which requires restrictive applicability conditions and could fail in many cases. Therefore, achieving sparse recovery with iterative regularization methods under a wider range of conditions has yet to be further explored.
To address this issue, we propose a novel iterative regularization algorithm, IRKSN, based on the k-support norm regularizer rather than the l1 norm. We provide conditions for sparse recovery with IRKSN, and compare them with traditional conditions for recovery with l1 norm regularizers. Additionally, we give an early stopping bound on the model error of IRKSN with explicit constants, achieving the standard linear rate for sparse recovery. Finally, we illustrate the applicability of our algorithm on several experiments, including a support recovery experiment with a correlated design matrix.
Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting
Authors: Deng Sen , Feng Yidan , Lin Haoneng , Fan Yiting , Lee Alex Pui-Wai , Hu Xiaowei , Qin Jing
Published: March 2024

Semi-supervised learning (SSL) is a powerful tool to address the challenge of insufficient annotated data in medical segmentation problems. However, existing semi-supervised methods mainly rely on internal knowledge for pseudo labeling, which is biased due to the distribution mismatch between the highly imbalanced labeled and unlabeled data. Segmenting left atrial appendage (LAA) from transesophageal echocardiogram (TEE) images is a typical medical image segmentation task featured by scarcity of professional annotations and diverse data distributions, for which existing SSL models cannot achieve satisfactory performance. In this paper, we propose a novel strategy to mitigate the inherent challenge of distribution mismatch in SSL by, for the first time, incorporating a large foundation model (i.e. SAM in our implementation) into an SSL model to improve the quality of pseudo labels. We further propose a new self-reconstruction mechanism to generate both noise-resilient prompts to demonically improve SAM’s generalization capability over TEE images and self-perturbations to stabilize the training process and reduce the impact of noisy labels. We conduct extensive experiments on an in-house TEE dataset; experimental results demonstrate that our method achieves better performance than state-of-the-art SSL models.
Non-parametric Representation Learning with Kernels
Authors: Esser Pascal , Fleissner Maximilian , Ghoshdastidar Debarghya
Published: March 2024

Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data. We argue that the classical representer theorems for supervised kernel machines are not always applicable for (self-supervised) representation learning, and present new representer theorems, which show that the representations learned by our kernel models can be expressed in terms of kernel matrices. We further derive generalisation error bounds for representation learning with kernel SSL and AE, and empirically evaluate the performance of these methods in both small data regimes as well as in comparison with neural network based models.
Improving GNN Calibration with Discriminative Ability: Insights and Strategies
Authors: Fang Yujie , Li Xin , Chen Qianyu , Wang Mingzhong
Published: March 2024

The widespread adoption of Graph Neural Networks (GNNs) has led to an increasing focus on their reliability. To address the issue of underconfidence in GNNs, various calibration methods have been developed to gain notable reductions in calibration error. However, we observe that existing approaches generally fail to enhance consistently, and in some cases even deteriorate, GNNs' ability to discriminate between correct and incorrect predictions. In this study, we advocate the significance of discriminative ability and the inclusion of relevant evaluation metrics. Our rationale is twofold: 1) Overlooking discriminative ability can inadvertently compromise the overall quality of the model; 2) Leveraging discriminative ability can significantly inform and improve calibration outcomes. Therefore, we thoroughly explore the reasons why existing calibration methods have ineffectiveness and even degradation regarding the discriminative ability of GNNs. Building upon these insights, we conduct GNN calibration experiments across multiple datasets using a straightforward example model, denoted as DC(GNN). Its excellent performance confirms the potential of integrating discriminative ability as a key consideration in the calibration of GNNs, thereby establishing a pathway toward more effective and reliable network calibration.
Latent Diffusion Transformer for Probabilistic Time Series Forecasting
Authors: Feng Shibo , Miao Chunyan , Zhang Zhong , Zhao Peilin
Published: March 2024

The probability prediction of multivariate time series is a notoriously challenging but practical task. This research proposes to condense high-dimensional multivariate time series forecasting into a problem of latent space time series generation, to improve the expressiveness of each timestamp and make forecasting more manageable. To solve the problem that the existing work is hard to extend to high-dimensional multivariate time series, we present a latent multivariate time series diffusion framework called Latent Diffusion Transformer (LDT), which consists of a symmetric statistics-aware autoencoder and a diffusion-based conditional generator, to implement this idea. Through careful design, the time series autoencoder can compress multivariate timestamp patterns into a concise latent representation by considering dynamic statistics. Then, the diffusion-based conditional generator is able to efficiently generate realistic multivariate timestamp values on a continuous latent space under a novel self-conditioning guidance which is modeled in a non-autoregressive way. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular high-dimensional multivariate time series datasets.
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
Authors: Giovanelli Joseph , Tornede Alexander , Tornede Tanja , Lindauer Marius
Published: March 2024

Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML).
In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption.
To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user.
Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front.
In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization.
Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator.
Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator.
Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach.
In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
Layer Compression of Deep Networks with Straight Flows
Authors: Gong Chengyue , Du Xiaocong , Bhushanam Bhargav , Wu Lemeng , Liu Xingchao , Choudhary Dhruv , Kejariwal Arun , Liu Qiang
Published: March 2024

Very deep neural networks lead to significantly better performance on various real tasks. However, it usually causes slow inference and is hard to be deployed on real-world devices. How to reduce the number of layers to save memory and to accelerate the inference is an eye-catching topic.
In this work, we introduce an intermediate objective, a continuous-time network, before distilling deep networks into shallow networks.
First, we distill a given deep network into a continuous-time neural flow model, which can be discretized with an ODE solver and the inference requires passing through the network multiple times.
By forcing the flow transport trajectory to be straight lines, we find that it is easier to compress the infinite step model into a one-step neural flow model, which only requires passing through the flow model once.
Secondly, we refine the one-step flow model together with the final head layer with knowledge distillation and finally, we can replace the given deep network with this one-step flow network.
Empirically, we demonstrate that our method outperforms direct distillation and other baselines on different model architectures (e.g. ResNet, ViT) on image classification and semantic segmentation tasks.
We also manifest that our distilled model naturally serves as an early-exit dynamic inference model.
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
Authors: Guo Yufei , Chen Yuanpei , Liu Xiaode , Peng Weihang , Zhang Yuhan , Huang Xuhui , Ma Zhe
Published: March 2024

The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretically and experimentally prove that the binary spike activation map cannot carry enough information, thus causing information loss and resulting in accuracy decreasing. To handle the problem, we propose a ternary spike neuron to transmit information. The ternary spike neuron can also enjoy the event-driven and multiplication-free operation advantages of the binary spike neuron but will boost the information capacity. Furthermore, we also embed a trainable factor in the ternary spike neuron to learn the suitable spike amplitude, thus our SNN will adopt different spike amplitudes along layers, which can better suit the phenomenon that the membrane potential distributions are different along layers. To retain the efficiency of the vanilla ternary spike, the trainable ternary spike SNN will be converted to a standard one again via a re-parameterization technique in the inference. Extensive experiments with several popular network structures over static and dynamic datasets show that the ternary spike can consistently outperform state-of-the-art methods. Our code is open-sourced at https://github.com/yfguo91/Ternary-Spike.
Not All Tasks Are Equally Difficult: Multi-Task Deep Reinforcement Learning with Dynamic Depth Routing
Authors: He Jinmin , Li Kai , Zang Yifan , Fu Haobo , Fu Qiang , Xing Junliang , Cheng Jian
Published: March 2024

Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a single policy. To enhance data efficiency by sharing parameters across multiple tasks, a common practice segments the network into distinct modules and trains a routing network to recombine these modules into task-specific policies. However, existing routing approaches employ a fixed number of modules for all tasks, neglecting that tasks with varying difficulties commonly require varying amounts of knowledge. This work presents a Dynamic Depth Routing (D2R) framework, which learns strategic skipping of certain intermediate modules, thereby flexibly choosing different numbers of modules for each task. Under this framework, we further introduce a ResRouting method to address the issue of disparate routing paths between behavior and target policies during off-policy training. In addition, we design an automatic route-balancing mechanism to encourage continued routing exploration for unmastered tasks without disturbing the routing of mastered ones. We conduct extensive experiments on various robotics manipulation tasks in the Meta-World benchmark, where D2R achieves state-of-the-art performance with significantly improved learning efficiency.
SoundCount: Sound Counting from Raw Audio with Dyadic Decomposition Neural Network
Authors: He Yuhang , Dai Zhuangzhuang , Trigoni Niki , Chen Long , Markham Andrew
Published: March 2024

In this paper, we study an underexplored, yet important and challenging problem: counting the number of distinct sounds in raw audio characterized by a high degree of polyphonicity. We do so by systematically proposing a novel end-to-end trainable neural network~(which we call DyDecNet, consisting of a dyadic decomposition front-end and backbone network), and quantifying the difficulty level of counting depending on sound polyphonicity. The dyadic decomposition front-end progressively decomposes the raw waveform dyadically along the frequency axis to obtain time-frequency representation in multi-stage, coarse-to-fine manner. Each intermediate waveform convolved by a parent filter is further processed by a pair of child filters that evenly split the parent filter's carried frequency response, with the higher-half child filter encoding the detail and lower-half child filter encoding the approximation. We further introduce an energy gain normalization to normalize sound loudness variance and spectrum overlap, and apply it to each intermediate parent waveform before feeding it to the two child filters. To better quantify sound counting difficulty level, we further design three polyphony-aware metrics: polyphony ratio, max polyphony and mean polyphony. We test DyDecNet on various datasets to show its superiority, and we further show dyadic decomposition network can be used as a general front-end to tackle other acoustic tasks.
Complete Neural Networks for Complete Euclidean Graphs
Authors: Hordan Snir , Amir Tal , Gortler Steven J. , Dym Nadav
Published: March 2024

Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems. Yet, to date, no architecture with polynomial complexity is known to be complete, that is, able to distinguish between any pair of non-isomorphic point clouds. We fill this theoretical gap by showing that point clouds can be completely determined, up to permutation and rigid motion, by applying the 3-WL graph isomorphism test to the point cloud's centralized Gram matrix. Moreover, we formulate an Euclidean variant of the 2-WL test and show that it is also sufficient to achieve completeness. We then show how our complete Euclidean WL tests can be simulated by an Euclidean graph neural network of moderate size and demonstrate their separation capability on highly symmetrical point clouds.
Structured Probabilistic Coding
Authors: Hu Dou , Wei Lingwei , Liu Yaxin , Zhou Wei , Hu Songlin
Published: March 2024

This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input related to the target task. SPC is an encoder-only probabilistic coding technology with a structured regularization from the target space. It can enhance the generalization ability of pre-trained language models for better language understanding. Specifically, our probabilistic coding simultaneously performs information encoding and task prediction in one module to more fully utilize the effective information from input data. It uses variational inference in the output space to reduce randomness and uncertainty. Besides, to better control the learning process of probabilistic representations, a structured regularization is proposed to promote uniformity across classes in the latent space. With the regularization term, SPC can preserve the Gaussian structure of the latent code and achieve better coverage of the hidden space with class uniformly. Experimental results on 12 natural language understanding tasks demonstrate that our SPC effectively improves the performance of pre-trained language models for classification and regression. Extensive experiments show that SPC can enhance the generalization capability, robustness to label noise, and clustering quality of output representations.
FedMut: Generalized Federated Learning via Stochastic Mutation
Authors: Hu Ming , Cao Yue , Li Anran , Li Zhiming , Liu Chengwei , Li Tianlin , Chen Mingsong , Liu Yang
Published: March 2024

Although Federated Learning (FL) enables collaborative model training without sharing the raw data of clients, it encounters low-performance problems caused by various heterogeneous scenarios. Due to the limitation of dispatching the same global model to clients for local training, traditional Federated Average (FedAvg)-based FL models face the problem of easily getting stuck into a sharp solution, which results in training a low-performance global model. To address this problem, this paper presents a novel FL approach named FedMut, which mutates the global model according to the gradient change to generate several intermediate models for the next round of training. Each intermediate model will be dispatched to a client for local training. Eventually, the global model converges into a flat area within the range of mutated models and has a well-generalization compared with the global model trained by FedAvg. Experimental results on well-known datasets demonstrate the effectiveness of our FedMut approach in various data heterogeneity scenarios.
PrefAce: Face-Centric Pretraining with Self-Structure Aware Distillation
Authors: Hu Siyuan , Wang Zheng , Hu Peng , Peng Xi , Wu Jie , Zhu Hongyuan , Ong Yew Soon
Published: March 2024

Video-based facial analysis is important for autonomous agents to understand human expressions and sentiments. However, limited labeled data is available to learn effective facial representations. This paper proposes a novel self-supervised face-centric pretraining framework, called PrefAce, which learns transferable video facial representation without labels. The self-supervised learning is performed with an effective landmark-guided global-local tube distillation. Meanwhile, a novel instance-wise update FaceFeat Cache is built to enforce more discriminative and diverse representations for downstream tasks. Extensive experiments demonstrate that the proposed framework learns universal instance-aware facial representations with fine-grained landmark details from videos. The point is that it can transfer across various facial analysis tasks, e.g., Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our framework also outperforms the state-of-the-art on various downstream tasks, even in low data regimes. Code is available at https://github.com/siyuan-h/PrefAce.
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Authors: Hu Zexin , Hu Kun , Mo Clinton , Pan Lei , Wang Zhiyong
Published: March 2024

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code is available at https://github.com/TDNResearch/TDN.
Energy Efficient Streaming Time Series Classification with Attentive Power Iteration
Authors: Huang Hao , Shah Tapan , Evans Scott , Yoo Shinjae
Published: March 2024

Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.
Higher-Order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Authors: Huang Yiming , Zeng Yujie , Wu Qiang , Lü Linyuan
Published: March 2024

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs. Codes and datasets are available at https://github.com/Yiminghh/HiGCN.
Instance-Conditional Timescales of Decay for Non-Stationary Learning
Authors: Jain Nishant , Shenoy Pradeep
Published: March 2024

Slow concept drift is a ubiquitous, yet under-studied problem in practical machine learning systems. In such settings, although recent data is more indicative of future data, naively prioritizing recent instances runs the risk of losing valuable information from the past. We propose an optimization-driven approach towards balancing instance importance over large training windows. First, we model instance relevance using a mixture of multiple timescales of decay, allowing us to capture rich temporal trends. Second, we learn an auxiliary scorer model that recovers the appropriate mixture of timescales as a function of the instance itself. Finally, we propose a nested optimization objective for learning the scorer, by which it maximizes forward transfer for the learned model. Experiments on a large real-world dataset of 39M photos over a 9 year period show upto 15% relative gains in accuracy compared to other robust learning baselines. We replicate our gains on two collections of real-world datasets for non-stationary learning, and extend our work to continual learning settings where, too, we beat SOTA methods by large margins.
Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systems
Authors: Jena Amit , Kalathil Dileep , Xie Le
Published: March 2024

This paper addresses the problem of Neural Network (NN) based adaptive stability certification in a dynamical system. The state-of-the-art methods, such as Neural Lyapunov Functions (NLFs), use NN-based formulations to assess the stability of a non-linear dynamical system and compute a Region of Attraction (ROA) in the state space. However, under parametric uncertainty, if the values of system parameters vary over time, the NLF methods fail to adapt to such changes and may lead to conservative stability assessment performance. We circumvent this issue by integrating Model Agnostic Meta-learning (MAML) with NLFs and propose meta-NLFs. In this process, we train a meta-function that adapts to any parametric shifts and updates into an NLF for the system with new test-time parameter values. We demonstrate the stability assessment performance of meta-NLFs on some standard benchmark autonomous dynamical systems.