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Found 20 papers, 17 papers with code
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus on selective retention of samples that incur moderately high losses.
Rethinking PGD Attack: Is Sign Function Necessary?
Based on that, we further propose a new raw gradient descent (RGD) algorithm that eliminates the use of sign.
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
Ranked #1 on
Crass AI
on BIG-bench
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset.
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce "Sparsity May Cry" Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge.
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation
This data-driven procedure yields L2O that can efficiently solve problems similar to those seen in training, that is, drawn from the same ``task distribution".
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Instead of compressing multiple tasks' knowledge into a single model, MoE separates the parameter space and only utilizes the relevant model pieces given task type and its input, which provides stabilized MTL training and ultra-efficient inference.
Sparse Winning Tickets are Data-Efficient Image Recognizers
Improving the performance of deep networks in data-limited regimes has warranted much attention.
Is Attention All That NeRF Needs?
While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages.
Ranked #1 on
Generalizable Novel View Synthesis
on LLFF
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs).
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels, often necessitating few to no validation samples, and is well generalized to other tasks such as image segmentation.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i. e., winning tickets) that can be trained in isolation to match full accuracy.
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights.
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership
The lottery ticket hypothesis (LTH) emerges as a promising framework to leverage a special sparse subnetwork (i. e., winning ticket) instead of a full model for both training and inference, that can lower both costs without sacrificing the performance.
Lottery Tickets can have Structural Sparsity
The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i. e., $\textit{winning tickets}$) that can be trained in isolation to match full accuracy.
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Based on our analysis, we summarize a guideline for parameter settings in regards of specific architecture characteristics, which we hope to catalyze the research progress on the topic of lottery ticket hypothesis.
Efficient Lottery Ticket Finding: Less Data is More
We observe that a high-quality winning ticket can be found with training and pruning the dense network on the very compact PrAC set, which can substantially save training iterations for the ticket finding process.
GANs Can Play Lottery Tickets Too
In this work, we for the first time study the existence of such trainable matching subnetworks in deep GANs.
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging, the training and inference of GNNs become increasingly expensive.
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.
