We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability
the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
This research Tackles Ricour's perspective of the willingness to forget and the
ability of memoey to step ahead the idea if past representation and looking into it as it is
one future project. This future which enables humans to recall and forget,
forgive and
tolerate .all of this is done under what's called by Ricour" the able ego". Afterwards, it
reaches a happy memory where by forgetfulness is one image out of many stored in the
memory. According to this, the research discusses the beginning of relation ship between
history and memory as it is perceived by Ricour, one of interrelation then it discusses the
relation of memory and language via its narratives. Narrative, according to Ricour, is
means which people use to express their unforgettable past experience, that they need to
resurrect the past via narrating and legislating it in stories. Then, it proceeds to handle the
idea of willingness to forget and the relation which links forgetfulness to memory, which is
one holistic relation, according to Ricour. This means that there is no forgetfulness without
recalling and vice versa. Despit that, as perceived by Ricour, memory has always been
living in confusion/ hesitation. In other word, you hesitate as to what to recall and what to
forget. This has forced or led this research of memory as it is one future project as Ricour
assures.it finally concludes with some findings that we sought to have in regards to
Ricour's perspective regarding this issus.
In this research a review of methods needed to make a vehicle follow a predefined path has been done where
the vehicle can follow the path and return to it if any deviation happened. The following Algorithms have
been applied to follow the path:
1
- Follow The Carrot [1] Algorithm
2- Pure Pursuit [2] Algorithm
3- Follow The Past [3] Algorithm
The implementation of these algorithms has been done by using statistical analysis software (MATLAB) to
make a robotic vehicle movement simulator that uses algorithms to follow a predefined path (recorded path).
We have found as a result that the (Follow the Carrot) algorithm is simple for understanding and applying,
on the other hand it causes larger errors in position and larger deviation from the path.
Also in the (Follow the Carrot) algorithm, the vehicle tends to take short cuts and moves directly towards the
goal point instead of moving on path curves.
Pure Pursuit algorithm also suffers from the same problems, but not in the same critical way, where we can
get better proportion results .Whereas the (Follow the Past) algorithm achieves a perfect path tracking for
applying certain conditions and study parameters.
