Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach

In this paper, we focus on the often-overlooked issue of embedding collapse in existing diffusion-based sequential recommendation models and propose ADRec, an innovative framework designed to mitigate this problem. Diverging from previous diffusion-based methods, ADRec applies an independent noise process to each token and performs diffusion across the entire target sequence during training. ADRec captures token interdependency through auto-regression while modeling per-token distributions through token-level diffusion. This dual approach enables the model to effectively capture both sequence dynamics and item representations, overcoming the limitations of existing methods. To further mitigate embedding collapse, we propose a three-stage training strategy: (1) pre-training the embedding weights, (2) aligning these weights with the ADRec backbone, and (3) fine-tuning the model. During inference, ADRec applies the denoising process only to the last token, ensuring that the meaningful patterns in historical interactions are preserved. Our comprehensive empirical evaluation across six datasets underscores the effectiveness of ADRec in enhancing both the accuracy and efficiency of diffusion-based sequential recommendation systems.

Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization

Developing robust speaker verification (SV) systems without speaker labels has been a longstanding challenge. Earlier research has highlighted a considerable performance gap between self-supervised and fully supervised approaches. In this paper, we enhance the non-contrastive self-supervised framework, Self-Distillation Prototypes Network (SDPN), by introducing dimension regularization that explicitly addresses the collapse problem through the application of regularization terms to speaker embeddings. Moreover, we integrate score normalization techniques from fully supervised SV to further bridge the gap toward supervised verification performance. SDPN with dimension regularization and score normalization sets a new state-of-the-art on the VoxCeleb1 speaker verification evaluation benchmark, achieving Equal Error Rate 1.29%, 1.60%, and 2.80% for trial VoxCeleb1-{O,E,H} respectively. These results demonstrate relative improvements of 28.3%, 19.6%, and 22.6% over the current best self-supervised methods, thereby advancing the frontiers of SV technology.

Sparse Point Clouds Assisted Learned Image Compression

In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques have explored the potential benefits of utilizing inter-modality correlations to enhance the image compression performance. In this paper, motivated by the recent success of learned image compression, we propose a new framework that uses sparse point clouds to assist in learned image compression in the autonomous driving scenario. We first project the 3D sparse point cloud onto a 2D plane, resulting in a sparse depth map. Utilizing this depth map, we proceed to predict camera images. Subsequently, we use these predicted images to extract multi-scale structural features. These features are then incorporated into learned image compression pipeline as additional information to improve the compression performance. Our proposed framework is compatible with various mainstream learned image compression models, and we validate our approach using different existing image compression methods. The experimental results show that incorporating point cloud assistance into the compression pipeline consistently enhances the performance.

DMOFC: Discrimination Metric-Optimized Feature Compression

Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mean Squared Error (MSE) between the reconstructed and original features, while neglecting the importance of inter-feature relationships. In this paper, we analyze the inter-feature relationships, focusing on feature discriminability in machine vision and underscoring its significance in feature compression. To maintain the feature discriminability of reconstructed features, we introduce a discrimination metric for feature compression. The discrimination metric is designed to ensure that the distance between features of the same category is smaller than the distance between features of different categories. Furthermore, we explore the relationship between the discrimination metric and the discriminability of the original features. Experimental results confirm the effectiveness of the proposed discrimination metric and reveal there exists a trade-off between the discrimination metric and the discriminability of the original features.

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space

We develop a theory of finite-dimensional polyhedral subsets over the Wasserstein space and optimization of functionals over them via first-order methods. Our main application is to the problem of mean-field variational inference, which seeks to approximate a distribution $\pi$ over $\mathbb{R}^d$ by a product measure $\pi^\star$. When $\pi$ is strongly log-concave and log-smooth, we provide (1) approximation rates certifying that $\pi^\star$ is close to the minimizer $\pi^\star_\diamond$ of the KL divergence over a \emph{polyhedral} set $\mathcal{P}_\diamond$, and (2) an algorithm for minimizing $\text{KL}(\cdot\|\pi)$ over $\mathcal{P}_\diamond$ with accelerated complexity $O(\sqrt \kappa \log(\kappa d/\varepsilon^2))$, where $\kappa$ is the condition number of $\pi$.

Revenge of MLP in Sequential Recommendation

Sequential recommendation models sequences of historical user-item interactive behaviors (or referred as token) to better infer dynamic preferences. Fueled by the improved neural network architectures such as RNN, CNN and Transformer, this field has enjoyed rapid performance boost in the past years. Recent progress on all-MLP models lights on an efficient method with less intensive computation, token-mixing MLP, to learn the transformation patterns among historical behaviors. However, due to the inherent fully-connection design that allows the unrestricted cross-token communication and ignores the chronological order, we find that directly applying token-mixing MLP into sequential recommendation leads to subpar performance. In this paper, we present a purely MLP-based sequential recommendation architecture TriMLP with a novel \underline{Tri}angular Mixer where the modified \underline{MLP} endows tokens with ordered interactions. As the cross-token interaction in MLP is actually matrix multiplication, Triangular Mixer drops the lower-triangle neurons in the weight matrix and thus blocks the connections from future tokens, which prevents information leakage and improves prediction capability under the standard auto-regressive training fashion. To further model long and short-term preferences on fine-grained level, the mixer adopts a dual-branch structure based on the delicate MLP described above, namely global and local mixing, to separately capture the sequential long-range dependencies and local patterns. Empirical study on 9 different scale datasets (contain 50K\textasciitilde20M behaviors) of various benchmarks, including MovieLens, Amazon and Tenrec, demonstrates that TriMLP attains promising and stable accuracy/efficiency trade-off, i.e., averagely surpasses several state-of-the-art baselines by 5.32\% and saves 8.44\% inference time cost.