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Found 24 papers, 3 papers with code
Regression-aware Inference with LLMs
Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks.
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models
Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}.
What do larger image classifiers memorise?
The success of modern neural networks has prompted study of the connection between memorisation and generalisation: overparameterised models generalise well, despite being able to perfectly fit (memorise) completely random labels.
Large Language Models with Controllable Working Memory
By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge.
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Pretrained large language models (LLMs) are general purpose problem solvers applicable to a diverse set of tasks with prompts.
Robust Distillation for Worst-class Performance
We show empirically that our robust distillation techniques not only achieve better worst-class performance, but also lead to Pareto improvement in the tradeoff between overall performance and worst-class performance compared to other baseline methods.
HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation
In this paper, we study training and inference of neural networks under the MPC setup.
Leveraging redundancy in attention with Reuse Transformers
Pairwise dot product-based attention allows Transformers to exchange information between tokens in an input-dependent way, and is key to their success across diverse applications in language and vision.
HD-cos Networks: Efficient Neural Architechtures for Secure Multi-Party Computation
In this paper, we study training and inference of neural networks under the MPC setup.
Teacher's pet: understanding and mitigating biases in distillation
Knowledge distillation is widely used as a means of improving the performance of a relatively simple student model using the predictions from a complex teacher model.
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation
State-of-the-art transformer models use pairwise dot-product based self-attention, which comes at a computational cost quadratic in the input sequence length.
Semantic Label Smoothing for Sequence to Sequence Problems
Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising.
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.
Text Segmentation by Cross Segment Attention
Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization.
Does label smoothing mitigate label noise?
Label smoothing is commonly used in training deep learning models, wherein one-hot training labels are mixed with uniform label vectors.
Ranked #13 on
Learning with noisy labels
on CIFAR-10N-Random3
Content Explorer: Recommending Novel Entities for a Document Writer
Background research is an essential part of document writing.
Extreme Multi-Label Classification
General Classification
+1
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest.
Using Gaussian Processes for Rumour Stance Classification in Social Media
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news.
Classifying Tweet Level Judgements of Rumours in Social Media
Social media is a rich source of rumours and corresponding community reactions.
