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Zhiqiang Shen

Publications and source records attributed to Zhiqiang Shen.

At least 127 records · Page 7Linked to original sources
arXiv2024

SlimPajama-DC: Understanding Data Combinations for LLM Training

This paper aims to understand the impacts of various data combinations (e.g., web text, Wikipedia, GitHub, books) on the pretraining of large language models using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T token RedPajama dataset contributed by Together. We have termed our research as SlimPajama-DC, an empirical analysis designed to uncover fundamental characteristics and best practices associated with employing SlimPajama in the training of large language models. During our research with SlimPajama, two pivotal observations emerged: (1) Global deduplication vs. local deduplication. We analyze and discuss how global (across different sources of datasets) and local (within the single source of dataset) deduplications affect the performance of trained models. (2) Proportions of highly-deduplicated multi-source datasets in the combination. To study this, we construct six configurations on SlimPajama dataset and train individual ones using 1.3B Cerebras-GPT model with Alibi and SwiGLU. Our best configuration outperforms the 1.3B model trained on RedPajama using the same number of training tokens by a significant margin. All our 1.3B models are trained on Cerebras 16$\times$ CS-2 cluster with a total of 80 PFLOP/s in bf16 mixed precision. We further extend our discoveries (such as increasing data diversity is crucial after global deduplication) on a 7B model with large batch-size training. Our SlimPajama-DC models are available at: https://huggingface.co/MBZUAI-LLM/SlimPajama-DC and the separate SlimPajama-DC datasets are available at: https://huggingface.co/datasets/MBZUAI-LLM/SlimPajama-627B-DC.

cs.CL↗
arXiv2024

Self-supervised Dataset Distillation: A Good Compression Is All You Need

Dataset distillation aims to compress information from a large-scale original dataset to a new compact dataset while striving to preserve the utmost degree of the original data informational essence. Previous studies have predominantly concentrated on aligning the intermediate statistics between the original and distilled data, such as weight trajectory, features, gradient, BatchNorm, etc. In this work, we consider addressing this task through the new lens of model informativeness in the compression stage on the original dataset pretraining. We observe that with the prior state-of-the-art SRe$^2$L, as model sizes increase, it becomes increasingly challenging for supervised pretrained models to recover learned information during data synthesis, as the channel-wise mean and variance inside the model are flatting and less informative. We further notice that larger variances in BN statistics from self-supervised models enable larger loss signals to update the recovered data by gradients, enjoying more informativeness during synthesis. Building on this observation, we introduce SC-DD, a simple yet effective Self-supervised Compression framework for Dataset Distillation that facilitates diverse information compression and recovery compared to traditional supervised learning schemes, further reaps the potential of large pretrained models with enhanced capabilities. Extensive experiments are conducted on CIFAR-100, Tiny-ImageNet and ImageNet-1K datasets to demonstrate the superiority of our proposed approach. The proposed SC-DD outperforms all previous state-of-the-art supervised dataset distillation methods when employing larger models, such as SRe$^2$L, MTT, TESLA, DC, CAFE, etc., by large margins under the same recovery and post-training budgets. Code is available at https://github.com/VILA-Lab/SRe2L/tree/main/SCDD/.

cs.CV↗
arXiv2024

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability. Code is available at https://github.com/vision-learning-acceleration-lab/i-mae.

cs.CV↗
arXiv2024

Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching

The lightweight "local-match-global" matching introduced by SRe2L successfully creates a distilled dataset with comprehensive information on the full 224x224 ImageNet-1k. However, this one-sided approach is limited to a particular backbone, layer, and statistics, which limits the improvement of the generalization of a distilled dataset. We suggest that sufficient and various "local-match-global" matching are more precise and effective than a single one and has the ability to create a distilled dataset with richer information and better generalization. We call this perspective "generalized matching" and propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this work, which aims to create a synthetic dataset with densities, ensuring consistency with the complete dataset across various backbones, layers, and statistics. As experimentally demonstrated, G-VBSM is the first algorithm to obtain strong performance across both small-scale and large-scale datasets. Specifically, G-VBSM achieves a performance of 38.7% on CIFAR-100 with 128-width ConvNet, 47.6% on Tiny-ImageNet with ResNet18, and 31.4% on the full 224x224 ImageNet-1k with ResNet18, under images per class (IPC) 10, 50, and 10, respectively. These results surpass all SOTA methods by margins of 3.9%, 6.5%, and 10.1%, respectively.

cs.CV↗
arXiv2024

Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving

We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. Traditional point-based 3D object detectors often employ architectures that rely on a progressive downsampling of points. While this method effectively reduces computational demands and increases receptive fields, it will compromise the preservation of crucial non-local information for accurate 3D object detection, especially in the complex driving scenarios. To address this, we introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector by efficiently modeling longer-range inter-dependency while including only a negligible overhead. Concretely, the Cross-Cluster Shifting operation enhances the conventional design by shifting partial channels from neighboring clusters, which enables richer interaction with non-local regions and thus enlarges the receptive field of clusters. We conduct extensive experiments on the KITTI, Waymo, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD in both detection accuracy and runtime efficiency.

cs.CV↗
arXiv2024

GMRT observations of OHM candidates from the ALFALFA survey

We present the results of our observations using the Giant Meterwave Radio Telescope (GMRT) to investigate the radio continuum and OH line emission of 10 OHM candidates from the Arecibo Legacy Fast ALFA (ALFALFA) survey. Among these candidates, we have identified two sources, AGC115713 and AGC249507, which display compact OH line emission that are spatially associated with radio continuum emission. These characteristics align with the typical properties of OHM galaxies. Furthermore, the infrared (IR) properties of these two galaxies are consistent with those of known OHM galaxies. %Importantly, these two sources have been independently confirmed by alternative methods. Of the two sources, AGC 249507 has been confirmed through optical redshift, whereas AGC 115713 meets a WISE color selection criterion in the literature, providing additional support for this source being an OHM galaxy rather than a nearby \HI galaxy. On the contrary, no significant spectral line emission were detected in the remaining eight OHM candidates using our full GMRT dataset. This suggests that the spectral line emission initially detected by the ALFALFA survey may have been significantly resolved in our high-resolution observations. Additionally, the absence of radio continuum emission in 6 candidates also distinguishes them from known OHM galaxies documented in the literature. These findings support the notion that OHM emission may be distributed on a subarcsecond scale, underscoring the utility of arcsecond-scale observations in confirming OHM candidates, particularly those lacking optical redshift data.

astro-ph.GA↗
arXiv2024

Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for efficient dataset condensation. The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution synthesis, and the ability to scale up to arbitrary evaluation network architectures. Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8% validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively. Our approach also surpasses MTT in terms of speed by approximately 52$\times$ (ConvNet-4) and 16$\times$ (ResNet-18) faster with less memory consumption of 11.6$\times$ and 6.4$\times$ during data synthesis. Our code and condensed datasets of 50, 200 IPC with 4K recovery budget are available at https://github.com/VILA-Lab/SRe2L.

cs.CV↗
arXiv2024

Precise Knowledge Transfer via Flow Matching

In this paper, we propose a novel knowledge transfer framework that introduces continuous normalizing flows for progressive knowledge transformation and leverages multi-step sampling strategies to achieve precision knowledge transfer. We name this framework Knowledge Transfer with Flow Matching (FM-KT), which can be integrated with a metric-based distillation method with any form (\textit{e.g.} vanilla KD, DKD, PKD and DIST) and a meta-encoder with any available architecture (\textit{e.g.} CNN, MLP and Transformer). By introducing stochastic interpolants, FM-KD is readily amenable to arbitrary noise schedules (\textit{e.g.}, VP-ODE, VE-ODE, Rectified flow) for normalized flow path estimation. We theoretically demonstrate that the training objective of FM-KT is equivalent to minimizing the upper bound of the teacher feature map or logit negative log-likelihood. Besides, FM-KT can be viewed as a unique implicit ensemble method that leads to performance gains. By slightly modifying the FM-KT framework, FM-KT can also be transformed into an online distillation framework OFM-KT with desirable performance gains. Through extensive experiments on CIFAR-100, ImageNet-1k, and MS-COCO datasets, we empirically validate the scalability and state-of-the-art performance of our proposed methods among relevant comparison approaches.

cs.CV↗
arXiv2024

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4

This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of large language models, examining their abilities, and enhancing user comprehension on the behaviors of different scales of large language models when feeding into different prompts. Extensive experiments are conducted on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. We hope that this work can provide a better guide for researchers working on the prompting of large language models. Project page is available at https://github.com/VILA-Lab/ATLAS.

cs.CL↗
arXiv2023

FerKD: Surgical Label Adaptation for Efficient Distillation

We present FerKD, a novel efficient knowledge distillation framework that incorporates partial soft-hard label adaptation coupled with a region-calibration mechanism. Our approach stems from the observation and intuition that standard data augmentations, such as RandomResizedCrop, tend to transform inputs into diverse conditions: easy positives, hard positives, or hard negatives. In traditional distillation frameworks, these transformed samples are utilized equally through their predictive probabilities derived from pretrained teacher models. However, merely relying on prediction values from a pretrained teacher, a common practice in prior studies, neglects the reliability of these soft label predictions. To address this, we propose a new scheme that calibrates the less-confident regions to be the context using softened hard groundtruth labels. Our approach involves the processes of hard regions mining + calibration. We demonstrate empirically that this method can dramatically improve the convergence speed and final accuracy. Additionally, we find that a consistent mixing strategy can stabilize the distributions of soft supervision, taking advantage of the soft labels. As a result, we introduce a stabilized SelfMix augmentation that weakens the variation of the mixed images and corresponding soft labels through mixing similar regions within the same image. FerKD is an intuitive and well-designed learning system that eliminates several heuristics and hyperparameters in former FKD solution. More importantly, it achieves remarkable improvement on ImageNet-1K and downstream tasks. For instance, FerKD achieves 81.2% on ImageNet-1K with ResNet-50, outperforming FKD and FunMatch by remarkable margins. Leveraging better pre-trained weights and larger architectures, our finetuned ViT-G14 even achieves 89.9%. Our code is available at https://github.com/szq0214/FKD/tree/main/FerKD.

cs.CV↗
arXiv2023

Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. Despite the success, most traditional VLMs-based methods are restricted by the assumption of partial source supervision or ideal vocabularies, which rarely satisfy the open-world scenario. In this paper, we aim at a more challenging setting, Realistic Zero-Shot Classification, which assumes no annotation but instead a broad vocabulary. To address this challenge, we propose the Self Structural Semantic Alignment (S^3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning. Our S^3A framework adopts a unique Cluster-Vote-Prompt-Realign (CVPR) algorithm, which iteratively groups unlabeled data to derive structural semantics for pseudo-supervision. Our CVPR process includes iterative clustering on images, voting within each cluster to identify initial class candidates from the vocabulary, generating discriminative prompts with large language models to discern confusing candidates, and realigning images and the vocabulary as structural semantic alignment. Finally, we propose to self-learn the CLIP image encoder with both individual and structural semantic alignment through a teacher-student learning strategy. Our comprehensive experiments across various generic and fine-grained benchmarks demonstrate that the S^3A method offers substantial improvements over existing VLMs-based approaches, achieving a more than 15% accuracy improvement over CLIP on average. Our codes, models, and prompts are publicly released at https://github.com/sheng-eatamath/S3A.

cs.CV↗
arXiv2023

LLM360: Towards Fully Transparent Open-Source LLMs

The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights or inference code, and technical reports increasingly limit their scope to high-level design choices and surface statistics. These choices hinder progress in the field by degrading transparency into the training of LLMs and forcing teams to rediscover many details in the training process. We present LLM360, an initiative to fully open-source LLMs, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community. The goal of LLM360 is to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible by everyone. As a first step of LLM360, we release two 7B parameter LLMs pre-trained from scratch, Amber and CrystalCoder, including their training code, data, intermediate checkpoints, and analyses (at https://www.llm360.ai). We are committed to continually pushing the boundaries of LLMs through this open-source effort. More large-scale and stronger models are underway and will be released in the future.

cs.CL↗
arXiv2023

Magnetic Fields in the Central Molecular Zone Influenced by Feedback and Weakly Correlated with Star Formation

Magnetic fields of molecular clouds in the Central Molecular Zone (CMZ) have been relatively underobserved at sub-parsec resolution. Here we report JCMT/POL2 observations of polarized dust emission in the CMZ, which reveal magnetic field structures in dense gas at ~0.5 pc resolution. The eleven molecular clouds in our sample including two in the western part of the CMZ (Sgr C and a far-side cloud candidate), four around the Galactic longitude 0 (the 50 km s-1 cloud, CO0.02-0.02, the `Stone' and the `Sticks & Straw' among the Three Little Pigs), and five along the Dust Ridge (G0.253+0.016, clouds b, c, d, and e/f), for each of which we estimate the magnetic field strength using the angular dispersion function method. The morphologies of magnetic fields in the clouds suggest potential imprints of feedback from expanding H II regions and young massive star clusters. A moderate correlation between the total viral parameter versus the star formation rate and the dense gas fraction of the clouds is found. A weak correlation between the mass-to-flux ratio and the star formation rate, and a weak anti-correlation between the magnetic field and the dense gas fraction are also found. Comparisons between magnetic fields and other dynamic components in clouds suggest a more dominant role of self-gravity and turbulence in determining the dynamical states of the clouds and affecting star formation at the studied scales.

astro-ph.GA↗
arXiv2023

Observational Evidence of a Centi-parsec Supermassive Black Hole Binary Existing in the Nearby Galaxy M81

Studying a centi-parsec supermassive black hole binary (SMBHB) would allow us to explore a new parameter space in active galactic nuclei, and these objects are also potential sources of gravitational waves. We report evidence that an SMBHB with an orbital period of about 30 yr may be resident in the nearby galactic nucleus M81. This orbital period and the known mass of M81 imply an orbital separation of about 0.02 pc. The jet emanating from the primary black hole showed a short period of jet wobbling at about 16.7 yr, superposing a long-term precession at a timescale of several hundred years. Periodic radio and X-ray outbursts were also found two times per orbital period, which could be explained by a double-peaked mass accretion rate variation per binary orbit. If confirmed, M81 would be one of the closest SMBHB candidates, providing a rare opportunity to study the final parsec problem.

astro-ph.GA↗
arXiv2023

Initializing Models with Larger Ones

Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. Code is available at https://github.com/OscarXZQ/weight-selection.

cs.LG↗
arXiv2023

The first Ka-band (26.1-35 GHz) blind line survey towards Orion KL

We conducted a Ka-band (26.1--35 GHz) line survey towards Orion KL using the TianMa 65-m Radio Telescope (TMRT). It is the first blind line survey in the Ka band, and achieves a sensitivity of mK level (1--3 mK at a spectral resolution of $\sim$1 km s$^{-1}$). In total, 592 Gaussian features are extracted. Among them, 257 radio recombination lines (RRLs) are identified. The maximum $Δn$ of RRLs of H, He and C are 20, 15, and 5, respectively. Through stacking, we have detected the $β$ lines of ion RRLs (RRLs of C$^+$ with possible contribution of other ions like O$^+$) for the first time, and tentative signal of the $γ$ lines of ion RRLs can also be seen on the stacked spectrum. Besides, 318 other line features were assigned to 37 molecular species, and ten of these species were not detected in the Q-band survey of TMRT. The vibrationally excited states of nine species were also detected. Emission of most species can be modeled under LTE. A number of transitions of E-CH3OH ($J_2-J_1$) display maser effects, which are confirmed by our modeling, and besides the bumping peak at $J\sim 6$ there is another peak at $J\sim 13$. Methylcyanoacetylene (CH$_3$C$_3$N) is detected in Orion KL for the first time. This work emphasizes that the Ka band, which was long-ignored for spectral line surveys, is very useful for surveying RRLs and molecular lines simultaneously.

astro-ph.GA↗
arXiv2023

Tentative detection of cyanoformamide NCCONH2 in space

The peptide-like molecules, cyanoformamide (NCCONH2), is the cyano (CN) derivative of formamide (NH2CHO). It is known to play a role in the synthesis of nucleic acid precursors under prebiotic conditions. In this paper, we present a tentative detection of NCCONH2 in the interstellar medium (ISM) with the Atacama Large Millimeter/submillimeter Array (ALMA) archive data. Ten unblended lines of NCCONH2 were seen around 3sigma noise levels toward Sagittarius B2(N1E), a position that is slightly offset from the continuum peak. The column density of NCCONH2 was estimated to be 2.4\times 10^15 cm ^-2, and the fractional abundance of NCCONH2 toward Sgr B2(N1E) was 6.9\times10^-10. The abundance ratio between NCCONH2 and NH2CHO is estimated to be ~0.01. We also searched for other peptide-like molecules toward Sgr B2(N1E). The abundances of NH2CHO, CH3NCO and CH3NHCHO toward Sgr B2(N1E) were about one tenth of those toward Sgr B2(N1S), while the abundances of CH3CONH2 was only one twentieth of that toward Sgr B2(N1S).

astro-ph.GA↗
arXiv2023

The ALMA-QUARKS survey: -- I. Survey description and data reduction

This paper presents an overview of the QUARKS survey, which stands for `Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures'. The QUARKS survey is observing 139 massive clumps covered by 156 pointings at ALMA Band 6 ($λ\sim$ 1.3 mm). In conjunction with data obtained from the ALMA-ATOMS survey at Band 3 ($λ\sim$ 3 mm), QUARKS aims to carry out an unbiased statistical investigation of massive star formation process within protoclusters down to a scale of 1000 au. This overview paper describes the observations and data reduction of the QUARKS survey, and gives a first look at an exemplar source, the mini-starburst Sgr B2(M). The wide-bandwidth (7.5 GHz) and high-angular-resolution (~0.3 arcsec) observations of the QUARKS survey allow to resolve much more compact cores than could be done by the ATOMS survey, and to detect previously unrevealed fainter filamentary structures. The spectral windows cover transitions of species including CO, SO, N$_2$D$^+$, SiO, H$_{30}α$, H$_2$CO, CH$_3$CN and many other complex organic molecules, tracing gas components with different temperatures and spatial extents. QUARKS aims to deepen our understanding of several scientific topics of massive star formation, such as the mass transport within protoclusters by (hub-)filamentary structures, the existence of massive starless cores, the physical and chemical properties of dense cores within protoclusters, and the feedback from already formed high-mass young protostars.

astro-ph.GA↗